上传了演示视频和彩页、海报

完成声源定位和前端设计

.gitignore

@@ -12,3 +12,60 @@
 # Built Visual Studio Code Extensions
 *.vsix
 
+# C++ 编译生成的文件
+*.o
+*.obj
+*.a
+*.so
+*.so.*
+*.dylib
+*.dll
+*.exe
+*.out
+*.app
+
+# Qt项目生成的文件
+*.moc
+moc_*.cpp
+moc_*.h
+qrc_*.cpp
+ui_*.h
+*.qm
+Makefile*
+*.qmlc
+*.jsc
+*_qmlcache.qrc
+
+# 编译目录
+bin/
+debug/
+release/
+obj/
+build/
+*build-*/
+
+# IDE相关文件
+.idea/
+*.swp
+*.swo
+*~
+*.bak
+*.orig
+.DS_Store
+
+# 临时文件
+tmp/
+temp/
+*.tmp
+*.log
+*.autosave
+
+# Linux 临时文件
+*~
+
+# 由OpenCV生成的文件
+*.jpg.aux
+*.png.aux
+
+# 保留特定文件，即使它们匹配上述模式
+!.gitignore

README.md

@@ -1,2 +1 @@
 # 无人机载视觉声觉融合战场感知系统
-

model/“启动声学监听与声源定位”用例实现.puml

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+@startuml
+title “启动声学监听与声源定位”用例实现顺序图
+
+actor "用户" as User
+boundary "MainUI" as UI
+control "AcousticManager" as Manager
+entity "AcousticProcessor" as Processor
+
+User -> UI: 启动声学监听功能
+activate UI
+
+UI -> Manager: startAcousticMonitoring()
+activate Manager
+
+Manager -> Processor: beginProcessing()
+activate Processor
+
+Processor -> Processor: 采集原始声学信号(麦克风阵列)
+Processor -> Processor: 数字滤波降噪预处理
+Processor -> Processor: 枪声识别(深度神经网络)
+Processor -> Processor: 计算声源方位和距离(TDOA)
+
+Processor --> Manager: 返回定位结果(/sound/location)
+deactivate Processor
+
+Manager --> UI: 更新并显示声源定位信息
+deactivate Manager
+
+UI --> User: 显示定位结果
+deactivate UI
+
+@enduml

model/“基于声源定位的视觉目标确认”用例实现.puml

@@ -0,0 +1,30 @@
+@startuml
+title “基于声源定位的视觉目标确认”用例实现顺序图
+
+actor "用户" as User
+control "VisionManager" as VM
+control "DroneController" as DC
+entity "VisionProcessor" as VP
+boundary "DetectionUI" as UI
+
+User -> VM: 触发视觉目标确认场景
+activate VM
+
+
+VM -> DC: adjustView(location)
+activate DC
+DC -> DC: 调整云台朝向目标区域
+DC -> VP: 请求图像采集
+deactivate DC
+
+activate VP
+VP -> VP: 采集多光谱图像 & YOLOv5s检测
+VP -> VP: 非极大值抑制(NMS)
+VP --> VM: 发送检测结果(/visual/detection)
+deactivate VP
+
+VM -> UI: 展示威胁目标标注及告警
+deactivate VM
+
+UI --> User: 显示视觉确认结果
+@enduml

model/“实时数据传输与指令交互”功能的实现.puml

@@ -0,0 +1,24 @@
+@startuml
+title “实时数据传输与指令交互”用例实现顺序图
+
+actor "指挥中心" as CommandCenter
+control "CommunicationManager" as ComMgr
+control "DroneComModule" as Drone
+database "飞控指令解析/传感器数据" as FCData
+
+CommandCenter -> ComMgr: 通过HTTPS(RESTful API)\n发送观测指令
+activate ComMgr
+
+ComMgr -> Drone: 转发飞控指令\n(WebSocket)
+activate Drone
+
+Drone -> FCData: 分析/执行指令调整\n(飞行轨迹 & 传感器参数)
+Drone --> ComMgr: 传回实时数据\n(声学特征 + 视觉流媒体)
+deactivate Drone
+
+ComMgr --> CommandCenter: 通知指挥中心\n数据回传完成
+deactivate ComMgr
+
+CommandCenter -> CommandCenter: 动态调整作战状态
+
+@enduml

model/“融合数据生成战术地图”用例实现.puml

@@ -0,0 +1,28 @@
+@startuml
+title “融合数据生成战术地图”用例实现顺序图
+
+actor "用户" as User
+control "MapManager" as MM
+entity "MapGenerator" as MG
+boundary "MapUI" as MUI
+
+User -> MM: 触发战术地图生成流程
+activate MM
+
+
+
+MM -> MG: fuseData(soundData, visualData)
+activate MG
+
+
+
+MG --> MM: 返回战术地图数据
+deactivate MG
+
+MM -> MUI: 渲染热力图 & 标注无人机位置
+activate MUI
+MUI --> User: 展示战术地图(威胁热力图、空间查询等)
+deactivate MUI
+
+deactivate MM
+@enduml

model/基于多模态融合的无人机战场隐蔽威胁定位系统用户界面跳转关系顺序图.puml

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+@startuml
+title 用户界面跳转关系顺序图
+
+actor "用户" as User
+participant "主界面" as MainUI
+participant "无人机自动前出侦测界面" as AutoUI
+participant "手动操纵无人机界面" as ManualUI
+participant "战场地图标注界面" as MapUI
+participant "系统设置界面" as SettingUI
+
+User -> MainUI: 打开系统主界面
+MainUI --> User: 显示主界面与功能导航
+
+User -> MainUI: 选择“无人机自动前出侦测”
+MainUI -> AutoUI: 跳转到自动前出侦测界面
+AutoUI --> User: 显示自动侦测信息
+
+User -> AutoUI: 切换手动模式
+AutoUI -> ManualUI: 跳转到手动操纵无人机界面
+ManualUI --> User: 显示手动操纵画面
+
+User -> ManualUI: 查看战场地图
+ManualUI -> MapUI: 跳转到战场地图标注界面
+MapUI --> User: 显示无人机位置与声源定位结果
+
+User -> MapUI: 进入系统设置
+MapUI -> SettingUI: 打开系统设置界面
+SettingUI --> User: 显示参数配置功能
+
+@enduml

model/数据库er图.puml

@@ -0,0 +1,54 @@
+@startuml
+title ER Diagram for "基于多模态融合的无人机战场隐蔽威胁定位系统"
+
+entity "T_Drone" as Drone {
+  -- Fields --
+  * id : VARCHAR(50) <<PK>>
+  status : ENUM
+  battery_level : INT
+  last_communication : TIMESTAMP
+}
+
+entity "T_AcousticData" as Acoustic {
+  -- Fields --
+  * id : INT <<PK>>
+  drone_id : VARCHAR(50)
+  timestamp : TIMESTAMP
+  location : GEOMETRY  // 声源定位信息（方位角+距离）
+  features : BLOB      // 声学特征数据
+  is_gunshot : BOOL
+}
+
+entity "T_VisualData" as Visual {
+  -- Fields --
+  * id : INT <<PK>>
+  drone_id : VARCHAR(50)
+  timestamp : TIMESTAMP
+  image_data : BLOB    // 图像数据
+  threats : BLOB       // 检测到的威胁信息
+}
+
+entity "T_Threat" as Threat {
+  -- Fields --
+  * id : INT <<PK>>
+  location : GEOMETRY
+  type : ENUM
+  confidence : FLOAT
+  discovery_time : TIMESTAMP
+}
+
+entity "T_CommandCenter" as CmdCenter {
+  -- Fields --
+  * id : VARCHAR(50) <<PK>>
+  connected_drones : VARCHAR
+  last_update : TIMESTAMP
+}
+
+' 关系定义：一架无人机可以采集多条声学或视觉数据
+Drone ||--o{ Acoustic : "drone_id"
+Drone ||--o{ Visual : "drone_id"
+
+' 指挥中心与无人机的关系（指挥中心可连接多架无人机）
+CmdCenter ||--o{ Drone : "connected_drones"
+
+@enduml

model/用户界面/主界面.html

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+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="UTF-8" />
+  <title>主界面</title>
+  <style>
+    body {
+      font-family: "Microsoft YaHei", sans-serif;
+      display: flex;
+      flex-direction: column;
+      justify-content: center;
+      align-items: center;
+      margin: 0;
+      padding: 0;
+      height: 100vh;
+      background: #f7f7f7;
+    }
+    header {
+      margin-bottom: 20px;
+    }
+    h1 {
+      font-size: 24px;
+      margin: 0;
+    }
+    .function-container {
+      display: flex;
+      flex-wrap: wrap;
+      margin-top: 10px;
+      justify-content: center;
+      gap: 26px;
+    }
+    .function-module {
+      background: #ffffff;
+      border: 1px solid #ddd;
+      border-radius: 10px;
+      width: 200px;
+      text-align: center;
+      padding: 20px;
+      cursor: pointer;
+      transition: box-shadow 0.2s;
+    }
+    .function-module:hover {
+      box-shadow: 0 0 8px rgba(0,0,0,0.15);
+    }
+    .function-icon {
+      font-size: 40px;
+      margin-bottom: 10px;
+    }
+    .function-desc {
+      font-size: 14px;
+      color: #666;
+    }
+    footer {
+      margin-top: 30px;
+    }
+    .help-btn {
+      background: #0078d4;
+      color: #fff;
+      padding: 10px 20px;
+      border-radius: 6px;
+      text-decoration: none;
+      font-size: 14px;
+    }
+    .help-btn:hover {
+      background: #005ea5;
+    }
+  </style>
+</head>
+<body>
+  <header>
+    <h1>“基于多模态融合的无人机战场隐蔽威胁定位系统”主界面</h1>
+  </header>
+  
+  <div class="function-container">
+    <div class="function-module" onclick="goToAutoDetection()">
+      <div class="function-icon">🚁</div>
+      <div>无人机自动前出侦测</div>
+      <div class="function-desc">快速侦测并定位战场隐蔽威胁</div>
+    </div>
+    <div class="function-module" onclick="goToManualUAV()">
+      <div class="function-icon">🎮</div>
+      <div>手动操纵无人机</div>
+      <div class="function-desc">自主飞行操控，实时掌握画面</div>
+    </div>
+    <div class="function-module" onclick="goToMapMarkup()">
+      <div class="function-icon">🗺️</div>
+      <div>战场地图标注</div>
+      <div class="function-desc">查看无人机位置及声源定位信息</div>
+    </div>
+    <div class="function-module" onclick="goToSettings()">
+      <div class="function-icon">⚙️</div>
+      <div>系统设置</div>
+      <div class="function-desc">灵活配置系统参数</div>
+    </div>
+  </div>
+
+  <footer>
+    <a class="help-btn" href="help.html">帮助</a>
+  </footer>
+
+  <script>
+    function goToAutoDetection() {
+      // 跳转到“无人机自动前出侦测”模块
+      alert('进入无人机自动前出侦测界面');
+    }
+    function goToManualUAV() {
+      // 跳转到“手动操纵无人机”模块
+      alert('进入手动操纵无人机界面');
+    }
+    function goToMapMarkup() {
+      // 跳转到“战场地图标注”模块
+      alert('进入战场地图标注界面');
+    }
+    function goToSettings() {
+      // 跳转到“系统设置”界面
+      alert('进入系统设置界面');
+    }
+  </script>
+</body>
+</html>

model/用户界面/战场地图标注界面.html

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+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="UTF-8">
+  <title>战场地图标注界面</title>
+  <style>
+    body {
+      font-family: "Microsoft YaHei", sans-serif;
+      margin: 0;
+      padding: 0;
+      display: flex;
+      flex-direction: column;
+      height: 100vh;
+    }
+    header, footer {
+      background-color: #f1f1f1;
+      padding: 10px;
+    }
+    main {
+      display: flex;
+      flex: 1;
+    }
+    .map-section {
+      flex: 2;
+      border-right: 1px solid #ddd;
+      position: relative;
+      display: flex;
+      flex-direction: column;
+    }
+    .map-area {
+      flex: 1;
+      background: #fafafa;
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      position: relative;
+    }
+    .map-area img {
+      max-width: 100%;
+      max-height: 100%;
+    }
+    .marker {
+      position: absolute;
+      width: 24px;
+      height: 24px;
+      background-repeat: no-repeat;
+      background-size: cover;
+      transform: translate(-50%, -50%);
+      cursor: pointer;
+    }
+    .uav-marker {
+      background-image: url('https://via.placeholder.com/24x24/0000FF?text=U');
+    }
+    .threat-marker {
+      background-image: url('https://via.placeholder.com/24x24/FF0000?text=T');
+    }
+    .map-tools {
+      background: rgba(255,255,255,0.8);
+      padding: 8px;
+      border: 1px solid #ddd;
+      border-radius: 4px;
+      margin: 10px;
+      align-self: flex-start;
+    }
+    .filter-panel {
+      flex: 1;
+      padding: 20px;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+    }
+    .filter-panel h3 {
+      margin-top: 0;
+    }
+    .filter-item {
+      margin-bottom: 8px;
+    }
+    .detail-button {
+      margin: 20px 0;
+      padding: 10px;
+      background: #0078d4;
+      color: #fff;
+      border: none;
+      border-radius: 6px;
+      cursor: pointer;
+    }
+    .detail-button:hover {
+      background: #005ea5;
+    }
+  </style>
+</head>
+<body>
+  <header>
+    <h2>战场地图标注界面</h2>
+  </header>
+
+  <main>
+    <section class="map-section">
+      <div class="map-tools">
+        <p>地图缩放/拖拽</p>
+        <p>比例尺：1:5000</p>
+        <p>坐标系：WGS-84</p>
+      </div>
+      <div class="map-area">
+        <!-- 地图区域，可替换为真实地图 -->
+        <img src="https://via.placeholder.com/600x400?text=Battlefield+Map" alt="作战地图"/>
+        <!-- 无人机位置标记示例 -->
+        <div class="marker uav-marker" style="top: 50%; left: 40%;"></div>
+        <!-- 威胁位置标记示例 -->
+        <div class="marker threat-marker" style="top: 50%; left: 60%;"></div>
+      </div>
+    </section>
+
+    <section class="filter-panel">
+      <div>
+        <h3>威胁等级筛选</h3>
+        <div class="filter-item">
+          <input type="checkbox" id="threatLow" checked>
+          <label for="threatLow">低等级威胁</label>
+        </div>
+        <div class="filter-item">
+          <input type="checkbox" id="threatMid" checked>
+          <label for="threatMid">中等级威胁</label>
+        </div>
+        <div class="filter-item">
+          <input type="checkbox" id="threatHigh" checked>
+          <label for="threatHigh">高等级威胁</label>
+        </div>
+        <button class="detail-button" onclick="viewThreatDetail()">威胁点详情</button>
+      </div>
+      <div>
+        <!-- 可放置其他功能或信息展示 -->
+      </div>
+    </section>
+  </main>
+
+  <footer>
+    FalconSense UAV System © 2025
+  </footer>
+
+  <script>
+    function viewThreatDetail() {
+      alert("查看特定威胁点的详细信息...");
+    }
+  </script>
+</body>
+</html>

model/用户界面/手动操纵无人机界面.html

@@ -0,0 +1,177 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="UTF-8" />
+  <title>手动操纵无人机界面</title>
+  <style>
+    body {
+      font-family: "Microsoft YaHei", sans-serif;
+      margin: 0;
+      padding: 0;
+      display: flex;
+      flex-direction: column;
+      height: 100vh;
+    }
+    header, footer {
+      background-color: #f1f1f1;
+      padding: 10px;
+    }
+    main {
+      display: flex;
+      flex: 1;
+    }
+    .video-section {
+      flex: 2;
+      border-right: 1px solid #ddd;
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      justify-content: center;
+    }
+    .control-section {
+      flex: 1;
+      padding: 20px;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+    }
+    .param-list {
+      background: #fff;
+      border: 1px solid #ddd;
+      border-radius: 6px;
+      padding: 10px;
+      margin-bottom: 10px;
+    }
+    .control-panel {
+      background: #fff;
+      border: 1px solid #ddd;
+      border-radius: 6px;
+      padding: 10px;
+      display: flex;
+      flex-direction: column;
+      gap: 12px;
+      align-items: center;
+    }
+    .control-panel h3 {
+      margin-top: 0;
+    }
+    /* 轮盘容器 */
+    .joystick-wrapper {
+      position: relative;
+      width: 120px;
+      height: 120px;
+      margin: 0 auto;
+      background: #eee;
+      border-radius: 50%;
+      display: flex;
+      align-items: center;
+      justify-content: center;
+    }
+    /* 轮盘主体 */
+    .joystick-base {
+      width: 100px;
+      height: 100px;
+      background: #ccc;
+      border-radius: 50%;
+      position: relative;
+    }
+    /* 用四个按钮模拟轮盘点击区域 */
+    .joystick-base button {
+      position: absolute;
+      width: 30px;
+      height: 30px;
+      border: none;
+      background: #999;
+      color: #fff;
+      font-size: 16px;
+      cursor: pointer;
+      border-radius: 50%;
+    }
+    .joystick-base button:hover {
+      background: #666;
+    }
+    .btn-up {
+      top: 0;
+      left: calc(50% - 15px);
+    }
+    .btn-down {
+      bottom: 0;
+      left: calc(50% - 15px);
+    }
+    .btn-left {
+      left: 0;
+      top: calc(50% - 15px);
+    }
+    .btn-right {
+      right: 0;
+      top: calc(50% - 15px);
+    }
+    .speed-control {
+      text-align: center;
+    }
+    .status-bar {
+      background: #fafafa;
+      border: 1px solid #ddd;
+      border-radius: 6px;
+      padding: 10px;
+    }
+    button {
+      margin: 0 4px;
+    }
+  </style>
+</head>
+<body>
+  <header>
+    <button onclick="returnHome()">一键返航</button>
+  </header>
+
+  <main>
+    <section class="video-section">
+      <!-- 实时视觉画面或占位图 -->
+      <img src="https://via.placeholder.com/640x360?text=Real-Time+UAV+Feed" alt="实时画面" />
+    </section>
+
+    <section class="control-section">
+      <div>
+        <div class="param-list">
+          <h3>声源定位参数</h3>
+          <p>方位角: <span id="azimuth">N/A</span></p>
+          <p>距离: <span id="distance">N/A</span></p>
+        </div>
+        <div class="control-panel">
+          <h3>无人机控制面板</h3>
+          <div class="joystick-wrapper">
+            <div class="joystick-base">
+              <button class="btn-up" onclick="move('up')">↑</button>
+              <button class="btn-left" onclick="move('left')">←</button>
+              <button class="btn-right" onclick="move('right')">→</button>
+              <button class="btn-down" onclick="move('down')">↓</button>
+            </div>
+          </div>
+          <div class="speed-control">
+            <label for="speedSlider">速度调节</label>
+            <input type="range" id="speedSlider" min="0" max="100" value="50" />
+          </div>
+        </div>
+      </div>
+      <div class="status-bar">
+        <p>电池电量: <span id="battery">100%</span></p>
+        <p>信号强度: <span id="signal">良好</span></p>
+      </div>
+    </section>
+  </main>
+
+  <footer>
+    FalconSense UAV System © 2025
+  </footer>
+
+  <script>
+    function returnHome() {
+      alert("启动一键返航功能...");
+    }
+    function move(direction) {
+      alert("无人机向 " + direction + " 移动");
+    }
+  </script>
+</body>
+</html>

model/用户界面/无人机自动前出侦测界面.html

@@ -0,0 +1,94 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="UTF-8" />
+  <title>无人机自动前出侦测界面</title>
+  <style>
+    body {
+      font-family: "Microsoft YaHei", sans-serif;
+      margin: 0; 
+      padding: 0; 
+      display: flex; 
+      flex-direction: column; 
+      height: 100vh;
+    }
+    header, footer {
+      background-color: #f1f1f1; 
+      padding: 10px; 
+    }
+    main {
+      display: flex; 
+      flex: 1; 
+    }
+    .video-section {
+      flex: 2; 
+      border-right: 1px solid #ddd;
+      display: flex; 
+      flex-direction: column; 
+      align-items: center; 
+      justify-content: center;
+    }
+    .params-section {
+      flex: 1; 
+      padding: 20px; 
+      display: flex; 
+      flex-direction: column;
+      justify-content: space-between;
+    }
+    .param-list {
+      border: 1px solid #ddd;
+      border-radius: 6px;
+      padding: 10px;
+      margin-bottom: 10px;
+      background: #fff;
+    }
+    .status-bar {
+      background: #fafafa;
+      border: 1px solid #ddd;
+      border-radius: 6px;
+      padding: 10px;
+    }
+    button {
+      margin: 0 8px;
+    }
+  </style>
+</head>
+<body>
+  <header>
+    <button onclick="startDetection()">开始侦测</button>
+    <button onclick="pauseDetection()">暂停侦测</button>
+  </header>
+
+  <main>
+    <section class="video-section">
+      <!-- 实时视觉画面，可使用视频流或占位图 -->
+      <img src="https://via.placeholder.com/640x360?text=Real-Time+UAV+Feed" alt="实时画面" />
+    </section>
+
+    <section class="params-section">
+      <div class="param-list">
+        <h3>声源定位参数</h3>
+        <p>方位角: <span id="azimuth">N/A</span></p>
+        <p>距离: <span id="distance">N/A</span></p>
+      </div>
+      <div class="status-bar">
+        <p>电池电量: <span id="battery">100%</span></p>
+        <p>信号强度: <span id="signal">良好</span></p>
+      </div>
+    </section>
+  </main>
+
+  <footer>
+    FalconSense UAV System © 2025
+  </footer>
+
+  <script>
+    function startDetection() {
+      alert("开始侦测");
+    }
+    function pauseDetection() {
+      alert("暂停侦测");
+    }
+  </script>
+</body>
+</html>

model/用户界面/系统设置界面.html

@@ -0,0 +1,184 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="UTF-8" />
+  <title>系统设置界面</title>
+  <style>
+    body {
+      font-family: "Microsoft YaHei", sans-serif;
+      margin: 0;
+      padding: 0;
+      display: flex;
+      flex-direction: column;
+      height: 100vh;
+    }
+    header, footer {
+      background-color: #f1f1f1;
+      padding: 10px;
+    }
+    main {
+      flex: 1;
+      display: flex;
+      background: #fafafa;
+    }
+    .settings-panel {
+      width: 70%;
+      margin: 20px auto;
+      background: #fff;
+      border: 1px solid #ddd;
+      border-radius: 6px;
+      padding: 20px;
+    }
+    h2 {
+      margin-top: 0;
+    }
+    .setting-section {
+      border: 1px solid #ddd;
+      border-radius: 6px;
+      padding: 10px;
+      margin-bottom: 20px;
+    }
+    .setting-section h3 {
+      margin-top: 0;
+      font-size: 16px;
+    }
+    .setting-item {
+      display: flex;
+      align-items: center;
+      margin: 8px 0;
+    }
+    .setting-item label {
+      width: 120px;
+    }
+    .setting-item input[type="text"],
+    .setting-item input[type="number"],
+    .setting-item input[type="range"],
+    .setting-item select {
+      flex: 1;
+      padding: 4px;
+      margin-right: 10px;
+    }
+    .setting-hint {
+      color: #777;
+      font-size: 12px;
+    }
+    .footer-buttons {
+      display: flex;
+      justify-content: flex-end;
+      gap: 10px;
+      margin-top: 20px;
+    }
+    .footer-buttons button {
+      padding: 8px 16px;
+      border: none;
+      border-radius: 4px;
+      cursor: pointer;
+    }
+    .save-button {
+      background: #0078d4;
+      color: #fff;
+    }
+    .save-button:hover {
+      background: #005ea5;
+    }
+    .reset-button {
+      background: #888;
+      color: #fff;
+    }
+    .reset-button:hover {
+      background: #666;
+    }
+  </style>
+</head>
+<body>
+  <header>
+    <h2>系统设置</h2>
+  </header>
+
+  <main>
+    <div class="settings-panel">
+      <!-- 无人机设置 -->
+      <div class="setting-section">
+        <h3>无人机设置</h3>
+        <div class="setting-item">
+          <label for="flightHeight">飞行高度</label>
+          <input type="number" id="flightHeight" placeholder="单位: 米" />
+          <span class="setting-hint">推荐值: 50 ~ 150</span>
+        </div>
+        <div class="setting-item">
+          <label for="flightSpeed">飞行速度限制</label>
+          <input type="number" id="flightSpeed" placeholder="单位: 米/秒" />
+          <span class="setting-hint">推荐值: 5 ~ 15</span>
+        </div>
+      </div>
+
+      <!-- 声学设置 -->
+      <div class="setting-section">
+        <h3>声学设置</h3>
+        <div class="setting-item">
+          <label for="micSensitivity">麦克风灵敏度</label>
+          <input type="range" id="micSensitivity" min="0" max="100" value="50" />
+          <span class="setting-hint">推荐值: 40 ~ 60</span>
+        </div>
+        <div class="setting-item">
+          <label for="noiseThreshold">声源过滤阈值</label>
+          <input type="number" id="noiseThreshold" placeholder="单位: dB" />
+          <span class="setting-hint">推荐值: 10 ~ 20</span>
+        </div>
+      </div>
+
+      <!-- 视觉设置 -->
+      <div class="setting-section">
+        <h3>视觉设置</h3>
+        <div class="setting-item">
+          <label for="cameraResolution">摄像头分辨率</label>
+          <select id="cameraResolution">
+            <option>1280x720</option>
+            <option>1920x1080</option>
+            <option>2560x1440</option>
+          </select>
+          <span class="setting-hint">推荐: 1920x1080</span>
+        </div>
+        <div class="setting-item">
+          <label for="frameRate">图像传输频率</label>
+          <input type="number" id="frameRate" placeholder="单位: FPS" />
+          <span class="setting-hint">推荐值: 15 ~ 30</span>
+        </div>
+      </div>
+
+      <!-- 通信设置 -->
+      <div class="setting-section">
+        <h3>通信设置</h3>
+        <div class="setting-item">
+          <label for="networkConfig">网络连接参数</label>
+          <input type="text" id="networkConfig" placeholder="如: 192.168.1.1:8080" />
+          <span class="setting-hint">输入IP地址与端口信息</span>
+        </div>
+        <div class="setting-item">
+          <label for="dataRate">数据传输速率</label>
+          <input type="number" id="dataRate" placeholder="单位: Kbps" />
+          <span class="setting-hint">推荐值: 500 ~ 2000</span>
+        </div>
+      </div>
+
+      <div class="footer-buttons">
+        <button class="save-button" onclick="saveSettings()">保存设置</button>
+        <button class="reset-button" onclick="resetSettings()">恢复默认</button>
+      </div>
+    </div>
+  </main>
+
+  <footer>
+    FalconSense UAV System © 2025
+  </footer>
+
+  <script>
+    function saveSettings() {
+      alert("设置已保存");
+    }
+    function resetSettings() {
+      alert("已恢复默认设置");
+    }
+  </script>
+</body>
+</html>

model/部署图.puml

@@ -0,0 +1,35 @@
+@startuml
+title 分布式部署架构
+
+node "指挥中心" <<device>> {
+  node "指挥终端" <<device>> {
+    artifact "Windows/Ubuntu OS"
+    artifact "主界面、自动侦测等前端界面" as GUI
+  }
+
+  node "计算节点" <<device>> {
+    artifact "Ubuntu OS"
+    artifact "定位与建图子系统"
+    artifact "MySQL 数据库管理系统"
+  }
+
+  node "数据库服务器" <<device>> {
+    artifact "专用数据库服务器环境"
+    artifact "MySQL DB"
+  }
+}
+
+node "战场环境" <<device>> {
+  node "无人机平台" <<device>> {
+    artifact "嵌入式Linux OS"
+    artifact "麦克风阵列、摄像头、声卡、GPS等"
+    artifact "声源定位与检测程序"
+  }
+}
+
+' 连接关系
+"无人机平台" -[#blue]-> "指挥终端" : WebSocket (4G/5G/军事频段)\n声学+视觉数据传输
+"指挥终端" -[#red]-> "计算节点" : HTTPS RESTful API
+"计算节点" -[#teal]-> "数据库服务器" : MySQL通信
+
+@enduml

src/声源定位代码/README_声源定位系统.md

@@ -0,0 +1,216 @@
+# 声源定位系统使用说明
+
+## 系统概述
+
+本系统由开发板端和PC端组成，实现声源定位和枪声识别功能。
+
+### 工作流程
+
+1. **开发板端**：开机后进入录音状态，持续录音并发送给PC端
+2. **PC端**：接收音频数据，使用audio-classification进行枪声识别
+3. **模式切换**：当识别到枪声时，PC端发送指令给开发板切换到定位模式
+4. **声源定位**：开发板在定位模式下实时发送声源定位数据给PC端
+5. **可视化**：PC端实时显示声源定位地图
+
+## 文件说明
+
+### 开发板端文件
+- `development_board_simple.py` - 简化版开发板端主程序（推荐使用）
+- `development_board.py` - 完整版开发板端主程序
+
+### PC端文件
+- `pc_server.py` - PC端服务器程序
+
+## 配置说明
+
+### 开发板端配置
+
+在 `development_board_simple.py` 中修改以下配置：
+
+```python
+# WiFi配置
+WIFI_SSID = "junzekeki"        # 替换为你的WiFi SSID
+WIFI_PASSWD = "234567890l"     # 替换为你的WiFi密码
+
+# PC端配置
+PC_IP = "192.168.1.100"        # PC端IP地址，需要根据实际情况修改
+PC_PORT_AUDIO = 12346          # 音频传输端口
+PC_PORT_CMD = 12347            # 指令传输端口
+PC_PORT_LOCATION = 12348       # 定位数据传输端口
+```
+
+### PC端配置
+
+在 `pc_server.py` 中修改以下配置：
+
+```python
+# 网络配置
+HOST = "0.0.0.0"         # 监听所有网络接口
+PORT_AUDIO = 12346       # 音频接收端口
+PORT_CMD = 12347         # 指令发送端口
+PORT_LOCATION = 12348    # 定位数据接收端口
+
+# 枪声识别配置
+GUNSHOT_THRESHOLD = 0.7  # 枪声识别阈值
+RECOGNITION_INTERVAL = 3.0  # 识别间隔(秒)
+```
+
+## 使用步骤
+
+### 1. 环境准备
+
+#### PC端环境
+```bash
+# 安装依赖
+pip install numpy matplotlib soundfile librosa
+
+# 如果使用audio-classification
+cd audio-classification
+pip install -r requirements.txt
+```
+
+#### 开发板端环境
+确保开发板已安装以下模块：
+- `fpioa_manager`
+- `Maix`
+- `board`
+- `network`
+- `socket`
+- `machine`
+
+### 2. 网络配置
+
+1. 确保开发板和PC在同一个WiFi网络下
+2. 获取PC的IP地址（Windows: `ipconfig`, Linux/Mac: `ifconfig`）
+3. 修改开发板端代码中的 `PC_IP` 为PC的实际IP地址
+
+### 3. 启动系统
+
+#### 启动PC端服务器
+```bash
+python pc_server.py
+```
+
+#### 启动开发板端
+将 `development_board_simple.py` 上传到开发板并运行：
+```bash
+python development_board_simple.py
+```
+
+### 4. 系统运行
+
+1. **初始化阶段**：
+   - 开发板自动连接WiFi
+   - 建立与PC端的Socket连接
+   - 进入录音模式
+
+2. **录音模式**：
+   - 开发板持续录音并发送音频数据
+   - PC端接收音频并进行枪声识别
+   - 控制台显示识别结果
+
+3. **定位模式**：
+   - 当检测到枪声时，PC端发送"START_LOCATION"指令
+   - 开发板切换到定位模式
+   - 实时发送声源定位数据
+   - PC端显示实时定位地图
+
+4. **模式切换**：
+   - 可通过关闭程序或发送"STOP_LOCATION"指令返回录音模式
+
+## 通信协议
+
+### 音频数据传输
+- 端口：12346
+- 格式：原始音频字节流（16位，16kHz，单声道）
+
+### 指令传输
+- 端口：12347
+- 格式：UTF-8字符串
+- 指令：
+  - `START_LOCATION` - 切换到定位模式
+  - `STOP_LOCATION` - 切换到录音模式
+
+### 定位数据传输
+- 端口：12348
+- 格式：`X,Y,强度,角度`（CSV格式）
+- 示例：`1.234,2.345,3.456,45.67`
+
+## 故障排除
+
+### 常见问题
+
+1. **WiFi连接失败**
+   - 检查WiFi SSID和密码是否正确
+   - 确保WiFi信号强度足够
+   - 检查开发板WiFi模块是否正常
+
+2. **Socket连接失败**
+   - 检查PC IP地址是否正确
+   - 确保防火墙未阻止端口
+   - 检查网络连接是否正常
+
+3. **音频识别失败**
+   - 检查audio-classification模型是否正确安装
+   - 确认模型文件路径是否正确
+   - 检查音频数据格式是否符合要求
+
+4. **定位数据异常**
+   - 检查麦克风阵列连接
+   - 确认麦克风阵列初始化是否成功
+   - 检查环境噪声是否过大
+
+### 调试方法
+
+1. **查看控制台输出**
+   - 开发板端和PC端都会输出详细的运行日志
+   - 根据日志信息定位问题
+
+2. **网络测试**
+   - 使用ping命令测试网络连通性
+   - 使用telnet测试端口是否开放
+
+3. **音频测试**
+   - 使用现有音频文件测试识别功能
+   - 检查音频数据是否正确接收
+
+## 扩展功能
+
+### 自定义音频识别
+可以修改PC端代码，使用其他音频分类模型或自定义识别逻辑。
+
+### 数据记录
+可以添加数据记录功能，保存音频文件和定位数据。
+
+### 多设备支持
+可以扩展支持多个开发板同时工作。
+
+### 远程控制
+可以添加Web界面进行远程控制和监控。
+
+## 注意事项
+
+1. **安全考虑**
+   - 本系统仅用于测试和演示
+   - 请勿用于实际的安全监控场景
+   - 注意保护个人隐私
+
+2. **性能优化**
+   - 根据实际需求调整识别间隔
+   - 优化网络传输参数
+   - 考虑使用更高效的音频编码
+
+3. **硬件要求**
+   - 开发板需要支持WiFi和麦克风阵列
+   - PC端需要足够的计算能力进行音频识别
+   - 建议使用有线网络连接以提高稳定性
+
+## 技术支持
+
+如有问题，请检查：
+1. 代码配置是否正确
+2. 网络连接是否正常
+3. 硬件连接是否牢固
+4. 依赖库是否正确安装
+
+更多技术细节请参考项目中的其他文档和代码注释。 

src/声源定位代码/audio-classification/.VSCodeCounter/2025-06-10_08-55-17/details.md

@@ -0,0 +1,69 @@
+# Details
+
+Date : 2025-06-10 08:55:17
+
+Directory e:\\pycharm_projects\\AudioClassification-Pytorch-master
+
+Total : 54 files,  6851 codes, 838 comments, 1201 blanks, all 8890 lines
+
+[Summary](results.md) / Details / [Diff Summary](diff.md) / [Diff Details](diff-details.md)
+
+## Files
+| filename | language | code | comment | blank | total |
+| :--- | :--- | ---: | ---: | ---: | ---: |
+| [README.md](/README.md) | Markdown | 302 | 0 | 72 | 374 |
+| [README\_en.md](/README_en.md) | Markdown | 231 | 0 | 45 | 276 |
+| [audio-classification-platform/README.md](/audio-classification-platform/README.md) | Markdown | 96 | 0 | 41 | 137 |
+| [audio-classification-platform/backend/app.py](/audio-classification-platform/backend/app.py) | Python | 255 | 35 | 61 | 351 |
+| [audio-classification-platform/backend/config.py](/audio-classification-platform/backend/config.py) | Python | 22 | 8 | 10 | 40 |
+| [audio-classification-platform/backend/requirements.txt](/audio-classification-platform/backend/requirements.txt) | pip requirements | 7 | 0 | 1 | 8 |
+| [audio-classification-platform/frontend/index.html](/audio-classification-platform/frontend/index.html) | HTML | 25 | 0 | 2 | 27 |
+| [audio-classification-platform/frontend/package.json](/audio-classification-platform/frontend/package.json) | JSON | 24 | 0 | 1 | 25 |
+| [audio-classification-platform/frontend/src/App.vue](/audio-classification-platform/frontend/src/App.vue) | Vue | 88 | 5 | 14 | 107 |
+| [audio-classification-platform/frontend/src/components/AudioRecorder.vue](/audio-classification-platform/frontend/src/components/AudioRecorder.vue) | Vue | 551 | 38 | 102 | 691 |
+| [audio-classification-platform/frontend/src/components/AudioRecorder\_new.vue](/audio-classification-platform/frontend/src/components/AudioRecorder_new.vue) | Vue | 811 | 48 | 162 | 1,021 |
+| [audio-classification-platform/frontend/src/components/AudioUpload.vue](/audio-classification-platform/frontend/src/components/AudioUpload.vue) | Vue | 580 | 28 | 107 | 715 |
+| [audio-classification-platform/frontend/src/components/HistoryList.vue](/audio-classification-platform/frontend/src/components/HistoryList.vue) | Vue | 513 | 25 | 79 | 617 |
+| [audio-classification-platform/frontend/src/components/PredictionResult.vue](/audio-classification-platform/frontend/src/components/PredictionResult.vue) | Vue | 803 | 22 | 117 | 942 |
+| [audio-classification-platform/frontend/src/main.js](/audio-classification-platform/frontend/src/main.js) | JavaScript | 13 | 1 | 5 | 19 |
+| [audio-classification-platform/frontend/src/router/index.js](/audio-classification-platform/frontend/src/router/index.js) | JavaScript | 14 | 0 | 4 | 18 |
+| [audio-classification-platform/frontend/src/utils/api.js](/audio-classification-platform/frontend/src/utils/api.js) | JavaScript | 156 | 25 | 33 | 214 |
+| [audio-classification-platform/frontend/src/views/HomePage.vue](/audio-classification-platform/frontend/src/views/HomePage.vue) | Vue | 692 | 40 | 110 | 842 |
+| [audio-classification-platform/frontend/vite.config.js](/audio-classification-platform/frontend/vite.config.js) | JavaScript | 32 | 0 | 2 | 34 |
+| [audio-classification-platform/start.bat](/audio-classification-platform/start.bat) | Batch | 20 | 0 | 5 | 25 |
+| [audio-classification-platform/start.sh](/audio-classification-platform/start.sh) | Shell Script | 26 | 3 | 8 | 37 |
+| [configs/augmentation.yml](/configs/augmentation.yml) | YAML | 21 | 21 | 5 | 47 |
+| [configs/cam++.yml](/configs/cam++.yml) | YAML | 43 | 33 | 5 | 81 |
+| [configs/ecapa\_tdnn.yml](/configs/ecapa_tdnn.yml) | YAML | 43 | 33 | 5 | 81 |
+| [configs/eres2net.yml](/configs/eres2net.yml) | YAML | 43 | 33 | 5 | 81 |
+| [configs/panns.yml](/configs/panns.yml) | YAML | 43 | 33 | 5 | 81 |
+| [configs/res2net.yml](/configs/res2net.yml) | YAML | 43 | 33 | 5 | 81 |
+| [configs/resnet\_se.yml](/configs/resnet_se.yml) | YAML | 43 | 33 | 5 | 81 |
+| [configs/tdnn.yml](/configs/tdnn.yml) | YAML | 43 | 33 | 5 | 81 |
+| [create\_data.py](/create_data.py) | Python | 75 | 9 | 16 | 100 |
+| [eval.py](/eval.py) | Python | 20 | 2 | 5 | 27 |
+| [extract\_features.py](/extract_features.py) | Python | 13 | 2 | 5 | 20 |
+| [infer.py](/infer.py) | Python | 17 | 1 | 6 | 24 |
+| [infer\_record.py](/infer_record.py) | Python | 40 | 7 | 12 | 59 |
+| [macls/\_\_init\_\_.py](/macls/__init__.py) | Python | 1 | 0 | 1 | 2 |
+| [macls/data\_utils/\_\_init\_\_.py](/macls/data_utils/__init__.py) | Python | 0 | 0 | 1 | 1 |
+| [macls/data\_utils/collate\_fn.py](/macls/data_utils/collate_fn.py) | Python | 17 | 4 | 3 | 24 |
+| [macls/data\_utils/featurizer.py](/macls/data_utils/featurizer.py) | Python | 88 | 36 | 9 | 133 |
+| [macls/data\_utils/reader.py](/macls/data_utils/reader.py) | Python | 114 | 33 | 11 | 158 |
+| [macls/metric/\_\_init\_\_.py](/macls/metric/__init__.py) | Python | 0 | 0 | 1 | 1 |
+| [macls/metric/metrics.py](/macls/metric/metrics.py) | Python | 9 | 1 | 3 | 13 |
+| [macls/optimizer/\_\_init\_\_.py](/macls/optimizer/__init__.py) | Python | 26 | 0 | 7 | 33 |
+| [macls/optimizer/scheduler.py](/macls/optimizer/scheduler.py) | Python | 42 | 0 | 7 | 49 |
+| [macls/predict.py](/macls/predict.py) | Python | 124 | 47 | 7 | 178 |
+| [macls/trainer.py](/macls/trainer.py) | Python | 338 | 99 | 20 | 457 |
+| [macls/utils/\_\_init\_\_.py](/macls/utils/__init__.py) | Python | 0 | 0 | 1 | 1 |
+| [macls/utils/checkpoint.py](/macls/utils/checkpoint.py) | Python | 113 | 40 | 10 | 163 |
+| [macls/utils/record.py](/macls/utils/record.py) | Python | 18 | 8 | 6 | 32 |
+| [macls/utils/utils.py](/macls/utils/utils.py) | Python | 99 | 16 | 17 | 132 |
+| [record\_audio.py](/record_audio.py) | Python | 10 | 0 | 5 | 15 |
+| [requirements.txt](/requirements.txt) | pip requirements | 17 | 0 | 1 | 18 |
+| [setup.py](/setup.py) | Python | 43 | 1 | 11 | 55 |
+| [tools/download\_language\_data.sh](/tools/download_language_data.sh) | Shell Script | 19 | 1 | 10 | 30 |
+| [train.py](/train.py) | Python | 25 | 1 | 5 | 31 |
+
+[Summary](results.md) / Details / [Diff Summary](diff.md) / [Diff Details](diff-details.md)

src/声源定位代码/audio-classification/.VSCodeCounter/2025-06-10_08-55-17/diff-details.md

@@ -0,0 +1,15 @@
+# Diff Details
+
+Date : 2025-06-10 08:55:17
+
+Directory e:\\pycharm_projects\\AudioClassification-Pytorch-master
+
+Total : 0 files,  0 codes, 0 comments, 0 blanks, all 0 lines
+
+[Summary](results.md) / [Details](details.md) / [Diff Summary](diff.md) / Diff Details
+
+## Files
+| filename | language | code | comment | blank | total |
+| :--- | :--- | ---: | ---: | ---: | ---: |
+
+[Summary](results.md) / [Details](details.md) / [Diff Summary](diff.md) / Diff Details

src/声源定位代码/audio-classification/.VSCodeCounter/2025-06-10_08-55-17/diff.csv

@@ -0,0 +1,2 @@
+"filename", "language", "", "comment", "blank", "total"
+"Total", "-", , 0, 0, 0

src/声源定位代码/audio-classification/.VSCodeCounter/2025-06-10_08-55-17/diff.md

@@ -0,0 +1,19 @@
+# Diff Summary
+
+Date : 2025-06-10 08:55:17
+
+Directory e:\\pycharm_projects\\AudioClassification-Pytorch-master
+
+Total : 0 files,  0 codes, 0 comments, 0 blanks, all 0 lines
+
+[Summary](results.md) / [Details](details.md) / Diff Summary / [Diff Details](diff-details.md)
+
+## Languages
+| language | files | code | comment | blank | total |
+| :--- | ---: | ---: | ---: | ---: | ---: |
+
+## Directories
+| path | files | code | comment | blank | total |
+| :--- | ---: | ---: | ---: | ---: | ---: |
+
+[Summary](results.md) / [Details](details.md) / Diff Summary / [Diff Details](diff-details.md)

src/声源定位代码/audio-classification/.VSCodeCounter/2025-06-10_08-55-17/diff.txt

@@ -0,0 +1,22 @@
+Date : 2025-06-10 08:55:17
+Directory : e:\pycharm_projects\AudioClassification-Pytorch-master
+Total : 0 files,  0 codes, 0 comments, 0 blanks, all 0 lines
+
+Languages
++----------+------------+------------+------------+------------+------------+
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++----------+------------+------------+------------+------------+------------+
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+
+Directories
++------+------------+------------+------------+------------+------------+
+| path | files      | code       | comment    | blank      | total      |
++------+------------+------------+------------+------------+------------+
++------+------------+------------+------------+------------+------------+
+
+Files
++----------+----------+------------+------------+------------+------------+
+| filename | language | code       | comment    | blank      | total      |
++----------+----------+------------+------------+------------+------------+
+| Total    |          |          0 |          0 |          0 |          0 |
++----------+----------+------------+------------+------------+------------+

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src/声源定位代码/audio-classification/.VSCodeCounter/2025-06-10_08-55-17/results.md

@@ -0,0 +1,50 @@
+# Summary
+
+Date : 2025-06-10 08:55:17
+
+Directory e:\\pycharm_projects\\AudioClassification-Pytorch-master
+
+Total : 54 files,  6851 codes, 838 comments, 1201 blanks, all 8890 lines
+
+Summary / [Details](details.md) / [Diff Summary](diff.md) / [Diff Details](diff-details.md)
+
+## Languages
+| language | files | code | comment | blank | total |
+| :--- | ---: | ---: | ---: | ---: | ---: |
+| Vue | 7 | 4,038 | 206 | 691 | 4,935 |
+| Python | 25 | 1,509 | 350 | 240 | 2,099 |
+| Markdown | 3 | 629 | 0 | 158 | 787 |
+| YAML | 8 | 322 | 252 | 40 | 614 |
+| JavaScript | 4 | 215 | 26 | 44 | 285 |
+| Shell Script | 2 | 45 | 4 | 18 | 67 |
+| HTML | 1 | 25 | 0 | 2 | 27 |
+| pip requirements | 2 | 24 | 0 | 2 | 26 |
+| JSON | 1 | 24 | 0 | 1 | 25 |
+| Batch | 1 | 20 | 0 | 5 | 25 |
+
+## Directories
+| path | files | code | comment | blank | total |
+| :--- | ---: | ---: | ---: | ---: | ---: |
+| . | 54 | 6,851 | 838 | 1,201 | 8,890 |
+| . (Files) | 11 | 793 | 23 | 183 | 999 |
+| audio-classification-platform | 19 | 4,728 | 278 | 864 | 5,870 |
+| audio-classification-platform (Files) | 3 | 142 | 3 | 54 | 199 |
+| audio-classification-platform\\backend | 3 | 284 | 43 | 72 | 399 |
+| audio-classification-platform\\frontend | 13 | 4,302 | 232 | 738 | 5,272 |
+| audio-classification-platform\\frontend (Files) | 3 | 81 | 0 | 5 | 86 |
+| audio-classification-platform\\frontend\\src | 10 | 4,221 | 232 | 733 | 5,186 |
+| audio-classification-platform\\frontend\\src (Files) | 2 | 101 | 6 | 19 | 126 |
+| audio-classification-platform\\frontend\\src\\components | 5 | 3,258 | 161 | 567 | 3,986 |
+| audio-classification-platform\\frontend\\src\\router | 1 | 14 | 0 | 4 | 18 |
+| audio-classification-platform\\frontend\\src\\utils | 1 | 156 | 25 | 33 | 214 |
+| audio-classification-platform\\frontend\\src\\views | 1 | 692 | 40 | 110 | 842 |
+| configs | 8 | 322 | 252 | 40 | 614 |
+| macls | 15 | 989 | 284 | 104 | 1,377 |
+| macls (Files) | 3 | 463 | 146 | 28 | 637 |
+| macls\\data_utils | 4 | 219 | 73 | 24 | 316 |
+| macls\\metric | 2 | 9 | 1 | 4 | 14 |
+| macls\\optimizer | 2 | 68 | 0 | 14 | 82 |
+| macls\\utils | 4 | 230 | 64 | 34 | 328 |
+| tools | 1 | 19 | 1 | 10 | 30 |
+
+Summary / [Details](details.md) / [Diff Summary](diff.md) / [Diff Details](diff-details.md)

src/声源定位代码/audio-classification/.VSCodeCounter/2025-06-10_08-55-17/results.txt

@@ -0,0 +1,107 @@
+Date : 2025-06-10 08:55:17
+Directory : e:\pycharm_projects\AudioClassification-Pytorch-master
+Total : 54 files,  6851 codes, 838 comments, 1201 blanks, all 8890 lines
+
+Languages
++------------------+------------+------------+------------+------------+------------+
+| language         | files      | code       | comment    | blank      | total      |
++------------------+------------+------------+------------+------------+------------+
+| Vue              |          7 |      4,038 |        206 |        691 |      4,935 |
+| Python           |         25 |      1,509 |        350 |        240 |      2,099 |
+| Markdown         |          3 |        629 |          0 |        158 |        787 |
+| YAML             |          8 |        322 |        252 |         40 |        614 |
+| JavaScript       |          4 |        215 |         26 |         44 |        285 |
+| Shell Script     |          2 |         45 |          4 |         18 |         67 |
+| HTML             |          1 |         25 |          0 |          2 |         27 |
+| pip requirements |          2 |         24 |          0 |          2 |         26 |
+| JSON             |          1 |         24 |          0 |          1 |         25 |
+| Batch            |          1 |         20 |          0 |          5 |         25 |
++------------------+------------+------------+------------+------------+------------+
+
+Directories
++------------------------------------------------------------------------------------------------------------------------------------+------------+------------+------------+------------+------------+
+| path                                                                                                                               | files      | code       | comment    | blank      | total      |
++------------------------------------------------------------------------------------------------------------------------------------+------------+------------+------------+------------+------------+
+| .                                                                                                                                  |         54 |      6,851 |        838 |      1,201 |      8,890 |
+| . (Files)                                                                                                                          |         11 |        793 |         23 |        183 |        999 |
+| audio-classification-platform                                                                                                      |         19 |      4,728 |        278 |        864 |      5,870 |
+| audio-classification-platform (Files)                                                                                              |          3 |        142 |          3 |         54 |        199 |
+| audio-classification-platform\backend                                                                                              |          3 |        284 |         43 |         72 |        399 |
+| audio-classification-platform\frontend                                                                                             |         13 |      4,302 |        232 |        738 |      5,272 |
+| audio-classification-platform\frontend (Files)                                                                                     |          3 |         81 |          0 |          5 |         86 |
+| audio-classification-platform\frontend\src                                                                                         |         10 |      4,221 |        232 |        733 |      5,186 |
+| audio-classification-platform\frontend\src (Files)                                                                                 |          2 |        101 |          6 |         19 |        126 |
+| audio-classification-platform\frontend\src\components                                                                              |          5 |      3,258 |        161 |        567 |      3,986 |
+| audio-classification-platform\frontend\src\router                                                                                  |          1 |         14 |          0 |          4 |         18 |
+| audio-classification-platform\frontend\src\utils                                                                                   |          1 |        156 |         25 |         33 |        214 |
+| audio-classification-platform\frontend\src\views                                                                                   |          1 |        692 |         40 |        110 |        842 |
+| configs                                                                                                                            |          8 |        322 |        252 |         40 |        614 |
+| macls                                                                                                                              |         15 |        989 |        284 |        104 |      1,377 |
+| macls (Files)                                                                                                                      |          3 |        463 |        146 |         28 |        637 |
+| macls\data_utils                                                                                                                   |          4 |        219 |         73 |         24 |        316 |
+| macls\metric                                                                                                                       |          2 |          9 |          1 |          4 |         14 |
+| macls\optimizer                                                                                                                    |          2 |         68 |          0 |         14 |         82 |
+| macls\utils                                                                                                                        |          4 |        230 |         64 |         34 |        328 |
+| tools                                                                                                                              |          1 |         19 |          1 |         10 |         30 |
++------------------------------------------------------------------------------------------------------------------------------------+------------+------------+------------+------------+------------+
+
+Files
++------------------------------------------------------------------------------------------------------------------------------------+------------------+------------+------------+------------+------------+
+| filename                                                                                                                           | language         | code       | comment    | blank      | total      |
++------------------------------------------------------------------------------------------------------------------------------------+------------------+------------+------------+------------+------------+
+| e:\pycharm_projects\AudioClassification-Pytorch-master\README.md                                                                   | Markdown         |        302 |          0 |         72 |        374 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\README_en.md                                                                | Markdown         |        231 |          0 |         45 |        276 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\README.md                                     | Markdown         |         96 |          0 |         41 |        137 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\backend\app.py                                | Python           |        255 |         35 |         61 |        351 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\backend\config.py                             | Python           |         22 |          8 |         10 |         40 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\backend\requirements.txt                      | pip requirements |          7 |          0 |          1 |          8 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\index.html                           | HTML             |         25 |          0 |          2 |         27 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\package.json                         | JSON             |         24 |          0 |          1 |         25 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\App.vue                          | Vue              |         88 |          5 |         14 |        107 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\components\AudioRecorder.vue     | Vue              |        551 |         38 |        102 |        691 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\components\AudioRecorder_new.vue | Vue              |        811 |         48 |        162 |      1,021 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\components\AudioUpload.vue       | Vue              |        580 |         28 |        107 |        715 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\components\HistoryList.vue       | Vue              |        513 |         25 |         79 |        617 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\components\PredictionResult.vue  | Vue              |        803 |         22 |        117 |        942 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\main.js                          | JavaScript       |         13 |          1 |          5 |         19 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\router\index.js                  | JavaScript       |         14 |          0 |          4 |         18 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\utils\api.js                     | JavaScript       |        156 |         25 |         33 |        214 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\src\views\HomePage.vue               | Vue              |        692 |         40 |        110 |        842 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\frontend\vite.config.js                       | JavaScript       |         32 |          0 |          2 |         34 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\start.bat                                     | Batch            |         20 |          0 |          5 |         25 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\audio-classification-platform\start.sh                                      | Shell Script     |         26 |          3 |          8 |         37 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\configs\augmentation.yml                                                    | YAML             |         21 |         21 |          5 |         47 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\configs\cam++.yml                                                           | YAML             |         43 |         33 |          5 |         81 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\configs\ecapa_tdnn.yml                                                      | YAML             |         43 |         33 |          5 |         81 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\configs\eres2net.yml                                                        | YAML             |         43 |         33 |          5 |         81 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\configs\panns.yml                                                           | YAML             |         43 |         33 |          5 |         81 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\configs\res2net.yml                                                         | YAML             |         43 |         33 |          5 |         81 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\configs\resnet_se.yml                                                       | YAML             |         43 |         33 |          5 |         81 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\configs\tdnn.yml                                                            | YAML             |         43 |         33 |          5 |         81 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\create_data.py                                                              | Python           |         75 |          9 |         16 |        100 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\eval.py                                                                     | Python           |         20 |          2 |          5 |         27 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\extract_features.py                                                         | Python           |         13 |          2 |          5 |         20 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\infer.py                                                                    | Python           |         17 |          1 |          6 |         24 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\infer_record.py                                                             | Python           |         40 |          7 |         12 |         59 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\__init__.py                                                           | Python           |          1 |          0 |          1 |          2 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\data_utils\__init__.py                                                | Python           |          0 |          0 |          1 |          1 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\data_utils\collate_fn.py                                              | Python           |         17 |          4 |          3 |         24 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\data_utils\featurizer.py                                              | Python           |         88 |         36 |          9 |        133 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\data_utils\reader.py                                                  | Python           |        114 |         33 |         11 |        158 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\metric\__init__.py                                                    | Python           |          0 |          0 |          1 |          1 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\metric\metrics.py                                                     | Python           |          9 |          1 |          3 |         13 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\optimizer\__init__.py                                                 | Python           |         26 |          0 |          7 |         33 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\optimizer\scheduler.py                                                | Python           |         42 |          0 |          7 |         49 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\predict.py                                                            | Python           |        124 |         47 |          7 |        178 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\trainer.py                                                            | Python           |        338 |         99 |         20 |        457 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\utils\__init__.py                                                     | Python           |          0 |          0 |          1 |          1 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\utils\checkpoint.py                                                   | Python           |        113 |         40 |         10 |        163 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\utils\record.py                                                       | Python           |         18 |          8 |          6 |         32 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\macls\utils\utils.py                                                        | Python           |         99 |         16 |         17 |        132 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\record_audio.py                                                             | Python           |         10 |          0 |          5 |         15 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\requirements.txt                                                            | pip requirements |         17 |          0 |          1 |         18 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\setup.py                                                                    | Python           |         43 |          1 |         11 |         55 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\tools\download_language_data.sh                                             | Shell Script     |         19 |          1 |         10 |         30 |
+| e:\pycharm_projects\AudioClassification-Pytorch-master\train.py                                                                    | Python           |         25 |          1 |          5 |         31 |
+| Total                                                                                                                              |                  |      6,851 |        838 |      1,201 |      8,890 |
++------------------------------------------------------------------------------------------------------------------------------------+------------------+------------+------------+------------+------------+

src/声源定位代码/audio-classification/.gitignore

@@ -0,0 +1,296 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+macls.egg-info/
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django/Flask stuff
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+instance/
+.webassets-cache
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+Pipfile.lock
+
+# PEP 582
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Node.js dependencies
+node_modules/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+lerna-debug.log*
+package-lock.json
+
+# Runtime data
+pids
+*.pid
+*.seed
+*.pid.lock
+
+# Coverage directory used by tools like istanbul
+coverage/
+
+# nyc test coverage
+.nyc_output
+
+# Grunt intermediate storage
+.grunt
+
+# Bower dependency directory
+bower_components
+
+# node-waf configuration
+.lock-wscript
+
+# Compiled binary addons
+build/Release
+
+# Dependency directories
+jspm_packages/
+
+# TypeScript cache
+*.tsbuildinfo
+
+# Optional npm cache directory
+.npm
+
+# Optional eslint cache
+.eslintcache
+
+# Microbundle cache
+.rpt2_cache/
+.rts2_cache_cjs/
+.rts2_cache_es/
+.rts2_cache_umd/
+
+# Optional REPL history
+.node_repl_history
+
+# Output of 'npm pack'
+*.tgz
+
+# Yarn Integrity file
+.yarn-integrity
+
+# dotenv environment variables file
+.env
+.env.test
+.env.local
+.env.production
+
+# parcel-bundler cache
+.cache
+.parcel-cache
+
+# Next.js build output
+.next
+
+# Nuxt.js build / generate output
+.nuxt
+
+# Gatsby files
+# Comment in the public line in if your project uses Gatsby
+# public
+
+# vuepress build output
+.vuepress/dist
+
+# Serverless directories
+.serverless/
+
+# FuseBox cache
+.fusebox/
+
+# DynamoDB Local files
+.dynamodb/
+
+# TernJS port file
+.tern-port
+
+# Stores VSCode versions used for testing VSCode extensions
+.vscode-test
+
+# Audio Classification specific ignores
+# Model files (usually large)
+*.pth
+*.pt
+*.h5
+*.ckpt
+*.pb
+*.onnx
+*.pkl
+*.joblib
+
+# Dataset directories (usually large audio files)
+dataset/
+dataset/*/audio/
+dataset/*/wav/
+dataset/*/mp3/
+dataset/*/flac/
+# Uncomment if you want to ignore all audio files
+# *.wav
+# *.mp3
+# *.flac
+# *.ogg
+# *.m4a
+# *.aac
+
+# Training artifacts and logs
+log/
+logs/
+output/
+outputs/
+uploads/
+results/
+checkpoints/
+models/
+pretrained_models/
+feature_models/
+runs/
+wandb/
+mlruns/
+.mlflow/
+
+# Temporary files
+temp/
+tmp/
+*.tmp
+test*.py
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# IDE files
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+
+# Audio processing temporary files
+*.spec
+*.mfcc
+*.mel
+
+# Frontend build files
+audio-classification-platform/frontend/dist/
+audio-classification-platform/frontend/build/
+audio-classification-platform/frontend/.vite/
+
+# Uploaded files directory
+audio-classification-platform/backend/uploads/
+
+# Local development configuration
+audio-classification-platform/backend/.env
+audio-classification-platform/frontend/.env.local

src/声源定位代码/audio-classification/LICENSE

@@ -0,0 +1,201 @@
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src/声源定位代码/audio-classification/README.md

@@ -0,0 +1,373 @@
+简体中文 | [English](./README_en.md)
+
+# 基于Pytorch实现的声音分类系统
+
+![python version](https://img.shields.io/badge/python-3.8+-orange.svg)
+![GitHub forks](https://img.shields.io/github/forks/yeyupiaoling/AudioClassification-Pytorch)
+![GitHub Repo stars](https://img.shields.io/github/stars/yeyupiaoling/AudioClassification-Pytorch)
+![GitHub](https://img.shields.io/github/license/yeyupiaoling/AudioClassification-Pytorch)
+![支持系统](https://img.shields.io/badge/支持系统-Win/Linux/MAC-9cf)
+
+# 前言
+
+本项目是基于Pytorch的声音分类项目，旨在实现对各种环境声音、动物叫声和语种的识别。项目提供了多种声音分类模型，如EcapaTdnn、PANNS、ResNetSE、CAMPPlus和ERes2Net，以支持不同的应用场景。此外，项目还提供了常用的Urbansound8K数据集测试报告和一些方言数据集的下载和使用例子。用户可以根据自己的需求选择适合的模型和数据集，以实现更准确的声音分类。项目的应用场景广泛，可以用于室外的环境监测、野生动物保护、语音识别等领域。同时，项目也鼓励用户探索更多的使用场景，以推动声音分类技术的发展和应用。
+
+**欢迎大家扫码入知识星球或者QQ群讨论，知识星球里面提供项目的模型文件和博主其他相关项目的模型文件，也包括其他一些资源。**
+
+<div align="center">
+  <img src="https://yeyupiaoling.cn/zsxq.png" alt="知识星球" width="400">
+  <img src="https://yeyupiaoling.cn/qq.png" alt="QQ群" width="400">
+</div>
+
+
+# 目录
+
+- [前言](#前言)
+- [项目特性](#项目特性)
+- [模型测试表](#模型测试表)
+- [安装环境](#安装环境)
+- [创建数据](#创建数据)
+- [修改预处理方法（可选）](#修改预处理方法可选)
+- [提取特征（可选）](#提取特征可选)
+- [训练模型](#训练模型)
+- [评估模型](#评估模型)
+- [预测](#预测)
+- [其他功能](#其他功能)
+
+
+# 使用准备
+
+ - Anaconda 3
+ - Python 3.11
+ - Pytorch 2.0.1
+ - Windows 11 or Ubuntu 22.04
+
+# 项目特性
+
+1. 支持模型：EcapaTdnn、PANNS、TDNN、Res2Net、ResNetSE、CAMPPlus、ERes2Net
+2. 支持池化层：AttentiveStatsPool(ASP)、SelfAttentivePooling(SAP)、TemporalStatisticsPooling(TSP)、TemporalAveragePooling(TAP)
+4. 支持预处理方法：MelSpectrogram、Spectrogram、MFCC、Fbank、Wav2vec2.0、WavLM
+
+**模型论文：**
+
+- EcapaTdnn：[ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://arxiv.org/abs/2005.07143v3)
+- PANNS：[PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/abs/1912.10211v5)
+- TDNN：[Prediction of speech intelligibility with DNN-based performance measures](https://arxiv.org/abs/2203.09148)
+- Res2Net：[Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169)
+- ResNetSE：[Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)
+- CAMPPlus：[CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://arxiv.org/abs/2303.00332v3)
+- ERes2Net：[An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification](https://arxiv.org/abs/2305.12838v1)
+
+# 模型测试表
+
+|      模型      | Params(M) | 预处理方法 |     数据集      | 类别数量 |   准确率   |   获取模型   |
+|:------------:|:---------:|:-----:|:------------:|:----:|:-------:|:--------:|
+|   ResNetSE   |    7.8    | Flank | UrbanSound8K |  10  | 0.96233 | 加入知识星球获取 |
+|  ERes2NetV2  |    5.4    | Flank | UrbanSound8K |  10  | 0.95662 | 加入知识星球获取 |
+|   CAMPPlus   |    7.1    | Flank | UrbanSound8K |  10  | 0.95454 | 加入知识星球获取 |
+|  EcapaTdnn   |    6.4    | Flank | UrbanSound8K |  10  | 0.95227 | 加入知识星球获取 |
+|   ERes2Net   |    6.6    | Flank | UrbanSound8K |  10  | 0.94292 | 加入知识星球获取 |
+|     TDNN     |    2.6    | Flank | UrbanSound8K |  10  | 0.93977 | 加入知识星球获取 |
+| PANNS（CNN10） |    5.2    | Flank | UrbanSound8K |  10  | 0.92954 | 加入知识星球获取 |
+|   Res2Net    |    5.0    | Flank | UrbanSound8K |  10  | 0.92580 | 加入知识星球获取 |
+
+**说明：**
+
+1. 使用的测试集为从数据集中每10条音频取一条，共874条。
+
+## 安装环境
+
+ - 首先安装的是Pytorch的GPU版本，如果已经安装过了，请跳过。
+```shell
+conda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1  pytorch-cuda=11.8 -c pytorch -c nvidia
+```
+
+ - 安装macls库。
+ 
+使用pip安装，命令如下：
+```shell
+python -m pip install macls -U -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+**建议源码安装**，源码安装能保证使用最新代码。
+```shell
+git clone https://github.com/yeyupiaoling/AudioClassification-Pytorch.git
+cd AudioClassification-Pytorch/
+pip install .
+```
+
+## 创建数据
+
+生成数据列表，用于下一步的读取需要，`audio_path`为音频文件路径，用户需要提前把音频数据集存放在`dataset/audio`目录下，每个文件夹存放一个类别的音频数据，每条音频数据长度在3秒以上，如 `dataset/audio/鸟叫声/······`。`audio`是数据列表存放的位置，生成的数据类别的格式为 `音频路径\t音频对应的类别标签`，音频路径和标签用制表符 `\t`分开。读者也可以根据自己存放数据的方式修改以下函数。
+
+以Urbansound8K为例，Urbansound8K是目前应用较为广泛的用于自动城市环境声分类研究的公共数据集，包含10个分类：空调声、汽车鸣笛声、儿童玩耍声、狗叫声、钻孔声、引擎空转声、枪声、手提钻、警笛声和街道音乐声。数据集下载地址：[UrbanSound8K.tar.gz](https://aistudio.baidu.com/aistudio/datasetdetail/36625)。以下是针对Urbansound8K生成数据列表的函数。如果读者想使用该数据集，请下载并解压到 `dataset`目录下，把生成数据列表代码改为以下代码。
+
+执行`create_data.py`即可生成数据列表，里面提供了生成多种数据集列表方式，具体看代码。
+```shell
+python create_data.py
+```
+
+生成的列表是长这样的，前面是音频的路径，后面是该音频对应的标签，从0开始，路径和标签之间用`\t`隔开。
+```shell
+dataset/UrbanSound8K/audio/fold2/104817-4-0-2.wav	4
+dataset/UrbanSound8K/audio/fold9/105029-7-2-5.wav	7
+dataset/UrbanSound8K/audio/fold3/107228-5-0-0.wav	5
+dataset/UrbanSound8K/audio/fold4/109711-3-2-4.wav	3
+```
+
+# 修改预处理方法（可选）
+
+配置文件中默认使用的是Fbank预处理方法，如果要使用其他预处理方法，可以修改配置文件中的安装下面方式修改，具体的值可以根据自己情况修改。如果不清楚如何设置参数，可以直接删除该部分，直接使用默认值。
+
+```yaml
+# 数据预处理参数
+preprocess_conf:
+  # 是否使用HF上的Wav2Vec2类似模型提取音频特征
+  use_hf_model: False
+  # 音频预处理方法，也可以叫特征提取方法
+  # 当use_hf_model为False时，支持：MelSpectrogram、Spectrogram、MFCC、Fbank
+  # 当use_hf_model为True时，指定的是HuggingFace的模型或者本地路径，比如facebook/w2v-bert-2.0或者./feature_models/w2v-bert-2.0
+  feature_method: 'Fbank'
+  # 当use_hf_model为False时，设置API参数，更参数查看对应API，不清楚的可以直接删除该部分，直接使用默认值。
+  # 当use_hf_model为True时，可以设置参数use_gpu，指定是否使用GPU提取特征
+  method_args:
+    sample_frequency: 16000
+    num_mel_bins: 80
+```
+
+# 提取特征（可选）
+
+在训练过程中，首先是要读取音频数据，然后提取特征，最后再进行训练。其中读取音频数据、提取特征也是比较消耗时间的，所以我们可以选择提前提取好取特征，训练模型的是就可以直接加载提取好的特征，这样训练速度会更快。这个提取特征是可选择，如果没有提取好的特征，训练模型的时候就会从读取音频数据，然后提取特征开始。提取特征步骤如下：
+
+1. 执行`extract_features.py`，提取特征，特征会保存在`dataset/features`目录下，并生成新的数据列表`train_list_features.txt`和`test_list_features.txt`。
+
+```shell
+python extract_features.py --configs=configs/cam++.yml --save_dir=dataset/features
+```
+
+2. 修改配置文件，将`dataset_conf.train_list`和`dataset_conf.test_list`修改为`train_list_features.txt`和`test_list_features.txt`。
+
+
+## 训练模型
+
+接着就可以开始训练模型了，创建 `train.py`。配置文件里面的参数一般不需要修改，但是这几个是需要根据自己实际的数据集进行调整的，首先最重要的就是分类大小`dataset_conf.num_class`，这个每个数据集的分类大小可能不一样，根据自己的实际情况设定。然后是`dataset_conf.batch_size`，如果是显存不够的话，可以减小这个参数。
+
+```shell
+# 单卡训练
+CUDA_VISIBLE_DEVICES=0 python train.py
+# 多卡训练
+CUDA_VISIBLE_DEVICES=0,1 torchrun --standalone --nnodes=1 --nproc_per_node=2 train.py
+```
+
+训练输出日志：
+```
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:14 - ----------- 额外配置参数 -----------
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - configs: configs/ecapa_tdnn.yml
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - local_rank: 0
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - pretrained_model: None
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - resume_model: None
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - save_model_path: models/
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - use_gpu: True
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:17 - ------------------------------------------------
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:19 - ----------- 配置文件参数 -----------
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:22 - dataset_conf:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:25 - 	aug_conf:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		noise_aug_prob: 0.2
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		noise_dir: dataset/noise
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		speed_perturb: True
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		volume_aug_prob: 0.2
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		volume_perturb: False
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:25 - 	dataLoader:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		batch_size: 64
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		num_workers: 4
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	do_vad: False
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:25 - 	eval_conf:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		batch_size: 1
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		max_duration: 20
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	label_list_path: dataset/label_list.txt
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	max_duration: 3
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	min_duration: 0.5
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	sample_rate: 16000
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:25 - 	spec_aug_args:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		freq_mask_width: [0, 8]
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		time_mask_width: [0, 10]
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	target_dB: -20
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	test_list: dataset/test_list.txt
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	train_list: dataset/train_list.txt
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	use_dB_normalization: True
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	use_spec_aug: True
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:22 - model_conf:
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	num_class: 10
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	pooling_type: ASP
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:22 - optimizer_conf:
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	learning_rate: 0.001
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	optimizer: Adam
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	scheduler: WarmupCosineSchedulerLR
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:25 - 	scheduler_args:
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:27 - 		max_lr: 0.001
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:27 - 		min_lr: 1e-05
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:27 - 		warmup_epoch: 5
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	weight_decay: 1e-06
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:22 - preprocess_conf:
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	feature_method: Fbank
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:25 - 	method_args:
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:27 - 		num_mel_bins: 80
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:27 - 		sample_frequency: 16000
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:22 - train_conf:
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:29 - 	log_interval: 10
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:29 - 	max_epoch: 30
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:31 - use_model: EcapaTdnn
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:32 - ------------------------------------------------
+[2023-08-07 22:54:22.213166 WARNING] trainer:__init__:67 - Windows系统不支持多线程读取数据，已自动关闭！
+==========================================================================================
+Layer (type:depth-idx)                   Output Shape              Param #
+==========================================================================================
+EcapaTdnn                                [1, 10]                   --
+├─Conv1dReluBn: 1-1                      [1, 512, 98]              --
+│    └─Conv1d: 2-1                       [1, 512, 98]              204,800
+│    └─BatchNorm1d: 2-2                  [1, 512, 98]              1,024
+├─Sequential: 1-2                        [1, 512, 98]              --
+│    └─Conv1dReluBn: 2-3                 [1, 512, 98]              --
+│    │    └─Conv1d: 3-1                  [1, 512, 98]              262,144
+│    │    └─BatchNorm1d: 3-2             [1, 512, 98]              1,024
+│    └─Res2Conv1dReluBn: 2-4             [1, 512, 98]              --
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+···································
+│    │    └─ModuleList: 3-56             --                        (recursive)
+│    │    └─ModuleList: 3-55             --                        (recursive)
+│    │    └─ModuleList: 3-56             --                        (recursive)
+│    │    └─ModuleList: 3-55             --                        (recursive)
+│    │    └─ModuleList: 3-56             --                        (recursive)
+│    └─Conv1dReluBn: 2-13                [1, 512, 98]              --
+│    │    └─Conv1d: 3-57                 [1, 512, 98]              262,144
+│    │    └─BatchNorm1d: 3-58            [1, 512, 98]              1,024
+│    └─SE_Connect: 2-14                  [1, 512, 98]              --
+│    │    └─Linear: 3-59                 [1, 256]                  131,328
+│    │    └─Linear: 3-60                 [1, 512]                  131,584
+├─Conv1d: 1-5                            [1, 1536, 98]             2,360,832
+├─AttentiveStatsPool: 1-6                [1, 3072]                 --
+│    └─Conv1d: 2-15                      [1, 128, 98]              196,736
+│    └─Conv1d: 2-16                      [1, 1536, 98]             198,144
+├─BatchNorm1d: 1-7                       [1, 3072]                 6,144
+├─Linear: 1-8                            [1, 192]                  590,016
+├─BatchNorm1d: 1-9                       [1, 192]                  384
+├─Linear: 1-10                           [1, 10]                   1,930
+==========================================================================================
+Total params: 6,188,490
+Trainable params: 6,188,490
+Non-trainable params: 0
+Total mult-adds (M): 470.96
+==========================================================================================
+Input size (MB): 0.03
+Forward/backward pass size (MB): 10.28
+Params size (MB): 24.75
+Estimated Total Size (MB): 35.07
+==========================================================================================
+[2023-08-07 22:54:26.726095 INFO   ] trainer:train:344 - 训练数据：8644
+[2023-08-07 22:54:30.092504 INFO   ] trainer:__train_epoch:296 - Train epoch: [1/30], batch: [0/4], loss: 2.57033, accuracy: 0.06250, learning rate: 0.00001000, speed: 19.02 data/sec, eta: 0:06:43
+```
+
+**训练可视化：**
+
+项目的根目录执行下面命令，并网页访问`http://localhost:8040/`，如果是服务器，需要修改`localhost`为服务器的IP地址。
+```shell
+visualdl --logdir=log --host=0.0.0.0
+```
+
+打开的网页如下：
+
+<br/>
+<div align="center">
+<img src="docs/images/log.jpg" alt="混淆矩阵" width="600">
+</div>
+
+
+
+# 评估模型
+
+执行下面命令执行评估。
+
+```shell
+python eval.py --configs=configs/bi_lstm.yml
+```
+
+评估输出如下：
+```shell
+[2025-02-03 15:13:25.469242 INFO   ] trainer:evaluate:461 - 成功加载模型：models/CAMPPlus_Fbank/best_model/model.pth
+100%|██████████████████████████████| 150/150 [00:00<00:00, 1281.96it/s]
+评估消耗时间：1s，loss：0.61840，accuracy：0.87333
+```
+
+评估会出来输出准确率，还保存了混淆矩阵图片，保存路径`output/images/`，如下。
+
+<br/>
+<div align="center">
+<img src="docs/images/image1.png" alt="混淆矩阵" width="600">
+</div>
+
+
+注意：如果类别标签是中文的，需要设置安装字体才能正常显示，一般情况下Windows无需安装，Ubuntu需要安装。如果Windows确实是缺少字体，只需要[字体文件](https://github.com/tracyone/program_font)这里下载`.ttf`格式的文件，复制到`C:\Windows\Fonts`即可。Ubuntu系统操作如下。
+
+1. 安装字体
+```shell
+git clone https://github.com/tracyone/program_font && cd program_font && ./install.sh
+```
+
+2. 执行下面Python代码
+```python
+import matplotlib
+import shutil
+import os
+
+path = matplotlib.matplotlib_fname()
+path = path.replace('matplotlibrc', 'fonts/ttf/')
+print(path)
+shutil.copy('/usr/share/fonts/MyFonts/simhei.ttf', path)
+user_dir = os.path.expanduser('~')
+shutil.rmtree(f'{user_dir}/.cache/matplotlib', ignore_errors=True)
+```
+
+# 预测
+
+在训练结束之后，我们得到了一个模型参数文件，我们使用这个模型预测音频。
+
+```shell
+python infer.py --audio_path=dataset/UrbanSound8K/audio/fold5/156634-5-2-5.wav
+```
+
+# 其他功能
+
+ - 为了方便读取录制数据和制作数据集，这里提供了录音程序`record_audio.py`，这个用于录制音频，录制的音频采样率为16000，单通道，16bit。
+
+```shell
+python record_audio.py
+```
+
+ - `infer_record.py`这个程序是用来不断进行录音识别，我们可以大致理解为这个程序在实时录音识别。通过这个应该我们可以做一些比较有趣的事情，比如把麦克风放在小鸟经常来的地方，通过实时录音识别，一旦识别到有鸟叫的声音，如果你的数据集足够强大，有每种鸟叫的声音数据集，这样你还能准确识别是那种鸟叫。如果识别到目标鸟类，就启动程序，例如拍照等等。
+
+```shell
+python infer_record.py --record_seconds=3
+```
+
+## 打赏作者
+<br/>
+<div align="center">
+<p>打赏一块钱支持一下作者</p>
+<img src="https://yeyupiaoling.cn/reward.png" alt="打赏作者" width="400">
+</div>
+
+# 参考资料
+
+1. https://github.com/PaddlePaddle/PaddleSpeech
+2. https://github.com/yeyupiaoling/PaddlePaddle-MobileFaceNets
+3. https://github.com/yeyupiaoling/PPASR
+4. https://github.com/alibaba-damo-academy/3D-Speaker

src/声源定位代码/audio-classification/README_en.md

@@ -0,0 +1,275 @@
+[简体中文](./README.md) | English
+
+# Sound classification system implemented in Pytorch
+
+![python version](https://img.shields.io/badge/python-3.8+-orange.svg)
+![GitHub forks](https://img.shields.io/github/forks/yeyupiaoling/AudioClassification-Pytorch)
+![GitHub Repo stars](https://img.shields.io/github/stars/yeyupiaoling/AudioClassification-Pytorch)
+![GitHub](https://img.shields.io/github/license/yeyupiaoling/AudioClassification-Pytorch)
+![支持系统](https://img.shields.io/badge/支持系统-Win/Linux/MAC-9cf)
+
+**Disclaimer, this document was obtained through machine translation, please check the original document [here](./README.md).**
+
+
+# Introduction
+
+This project is a sound classification project based on Pytorch, aiming to realize the recognition of various environmental sounds, animal calls and languages. Several sound classification models such as EcapaTdnn, PANNS, ResNetSE, CAMPPlus, and ERes2Net are provided to support different application scenarios. In addition, the project also provides the commonly used Urbansound8K dataset test report and some dialect datasets download and use examples. Users can choose suitable models and datasets according to their needs to achieve more accurate sound classification. The project has a wide range of application scenarios, and can be used in outdoor environmental monitoring, wildlife protection, speech recognition and other fields. At the same time, the project also encourages users to explore more usage scenarios to promote the development and application of sound classification technology.
+
+
+# Environment
+
+ - Anaconda 3
+ - Python 3.11
+ - Pytorch 2.0.1
+ - Windows 11 or Ubuntu 22.04
+
+# Project Features
+
+1. Supporting models: EcapaTdnn、PANNS、TDNN、Res2Net、ResNetSE、CAMPPlus、ERes2Net
+2. Supporting pooling: AttentiveStatsPool(ASP)、SelfAttentivePooling(SAP)、TemporalStatisticsPooling(TSP)、TemporalAveragePooling(TAP)
+3. Support preprocessing methods: MelSpectrogram、Spectrogram、MFCC、Fbank、Wav2vec2.0、WavLM
+
+**Model Paper：**
+
+- EcapaTdnn：[ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://arxiv.org/abs/2005.07143v3)
+- PANNS：[PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/abs/1912.10211v5)
+- TDNN：[Prediction of speech intelligibility with DNN-based performance measures](https://arxiv.org/abs/2203.09148)
+- Res2Net：[Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169)
+- ResNetSE：[Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)
+- CAMPPlus：[CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://arxiv.org/abs/2303.00332v3)
+- ERes2Net：[An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification](https://arxiv.org/abs/2305.12838v1)
+
+# Model Test
+
+|    Model     | Params(M) | Preprocessing method |   Dataset    | Number Class | Accuracy |
+|:------------:|:---------:|:--------------------:|:------------:|:------------:|:--------:|
+|   ResNetSE   |    7.8    |        Flank         | UrbanSound8K |      10      | 0.96233  |
+|  ERes2NetV2  |    5.4    |        Flank         | UrbanSound8K |      10      | 0.95662  |
+|   CAMPPlus   |    7.1    |        Flank         | UrbanSound8K |      10      | 0.95454  |
+|  EcapaTdnn   |    6.4    |        Flank         | UrbanSound8K |      10      | 0.95227  |
+|   ERes2Net   |    6.6    |        Flank         | UrbanSound8K |      10      | 0.94292  |
+|     TDNN     |    2.6    |        Flank         | UrbanSound8K |      10      | 0.93977  |
+| PANNS（CNN10） |    5.2    |        Flank         | UrbanSound8K |      10      | 0.92954  |
+|   Res2Net    |    5.0    |        Flank         | UrbanSound8K |      10      | 0.92580  |
+
+## Installation Environment
+
+ - The GPU version of Pytorch will be installed first, please skip it if you already have it installed.
+```shell
+conda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1  pytorch-cuda=11.8 -c pytorch -c nvidia
+```
+
+ - Install macls.
+ 
+Install it using pip with the following command:
+```shell
+python -m pip install macls -U -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+**Source installation is recommended**, which ensures that the latest code is used.
+```shell
+git clone https://github.com/yeyupiaoling/AudioClassification_Pytorch.git
+cd AudioClassification_Pytorch/
+python setup.py install
+```
+
+## Preparing Data
+
+The `audio_path` is the audio file path. The user needs to store the audio dataset in the `dataset/audio` directory in advance. Each folder stores a category of audio data, and the length of each audio data is more than 3 seconds. For example, `dataset/audio/ bird song /······`. `audio` is where the data list is stored, and the format of the generated data category is`audio_path\tcategory_label_audio`, and the audio path and label are separated by a TAB character `\t`. You can also modify the following functions depending on how you store your data:
+
+Taking Urbansound8K as an example, it is a widely used public dataset for automatic urban environmental sound classification research. Urbansound8K contains 10 categories: air condition sound, car whistle sound, children playing sound, dog bark, drilling sound, engine idling sound, gun sound, jackdrill, siren sound, and street music sound. Data set download address: [UrbanSound8K](https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz). Here is the function to generate a list of data for Urbansound8K. If you want to use this dataset, please download and unzip it into the `dataset` directory and change the code to generate the list of data as follows.
+
+`create_data.py` can be used to generate a list of data sets. There are many ways to generate a list of data sets.
+```shell
+python create_data.py
+```
+
+The resulting list looks like this, with the path to the audio followed by the tag for that audio, starting at 0, and separated by `\t`.
+```shell
+dataset/UrbanSound8K/audio/fold2/104817-4-0-2.wav	4
+dataset/UrbanSound8K/audio/fold9/105029-7-2-5.wav	7
+dataset/UrbanSound8K/audio/fold3/107228-5-0-0.wav	5
+dataset/UrbanSound8K/audio/fold4/109711-3-2-4.wav	3
+```
+
+# Change preprocessing methods
+
+By default, the Fbank preprocessing method is used in the configuration file. If you want to use other preprocessing methods, you can modify the following installation in the configuration file, and the specific value can be modified according to your own situation. If it's not clear how to set the parameters, you can remove that section and just use the default values.
+
+```yaml
+# 数据预处理参数
+preprocess_conf:
+  # 是否使用HF上的Wav2Vec2类似模型提取音频特征
+  use_hf_model: False
+  # 音频预处理方法，也可以叫特征提取方法
+  # 当use_hf_model为False时，支持：MelSpectrogram、Spectrogram、MFCC、Fbank
+  # 当use_hf_model为True时，指定的是HuggingFace的模型或者本地路径，比如facebook/w2v-bert-2.0或者./feature_models/w2v-bert-2.0
+  feature_method: 'Fbank'
+  # 当use_hf_model为False时，设置API参数，更参数查看对应API，不清楚的可以直接删除该部分，直接使用默认值。
+  # 当use_hf_model为True时，可以设置参数use_gpu，指定是否使用GPU提取特征
+  method_args:
+    sample_frequency: 16000
+    num_mel_bins: 80
+```
+
+## 训练
+
+Now we can train the model. We will create `train.py`. The parameters in the configuration file generally do not need to be modified, but these few need to be adjusted according to your actual dataset. The first and most important is the class size `dataset_conf.num_class`, which may be different for each dataset. Then there is` dataset_conf.batch_size `, which can be reduced if memory is insufficient.
+
+```shell
+# Single GPU training
+CUDA_VISIBLE_DEVICES=0 python train.py
+# Multi GPU training
+CUDA_VISIBLE_DEVICES=0,1 torchrun --standalone --nnodes=1 --nproc_per_node=2 train.py
+```
+
+Train log:
+```
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:14 - ----------- 额外配置参数 -----------
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - configs: configs/ecapa_tdnn.yml
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - local_rank: 0
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - pretrained_model: None
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - resume_model: None
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - save_model_path: models/
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:16 - use_gpu: True
+[2023-08-07 22:54:22.148973 INFO   ] utils:print_arguments:17 - ------------------------------------------------
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:19 - ----------- 配置文件参数 -----------
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:22 - dataset_conf:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:25 - 	aug_conf:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		noise_aug_prob: 0.2
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		noise_dir: dataset/noise
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		speed_perturb: True
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		volume_aug_prob: 0.2
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		volume_perturb: False
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:25 - 	dataLoader:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		batch_size: 64
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		num_workers: 4
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	do_vad: False
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:25 - 	eval_conf:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		batch_size: 1
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		max_duration: 20
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	label_list_path: dataset/label_list.txt
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	max_duration: 3
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	min_duration: 0.5
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:29 - 	sample_rate: 16000
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:25 - 	spec_aug_args:
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		freq_mask_width: [0, 8]
+[2023-08-07 22:54:22.202166 INFO   ] utils:print_arguments:27 - 		time_mask_width: [0, 10]
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	target_dB: -20
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	test_list: dataset/test_list.txt
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	train_list: dataset/train_list.txt
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	use_dB_normalization: True
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:29 - 	use_spec_aug: True
+[2023-08-07 22:54:22.203167 INFO   ] utils:print_arguments:22 - model_conf:
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	num_class: 10
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	pooling_type: ASP
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:22 - optimizer_conf:
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	learning_rate: 0.001
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	optimizer: Adam
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	scheduler: WarmupCosineSchedulerLR
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:25 - 	scheduler_args:
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:27 - 		max_lr: 0.001
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:27 - 		min_lr: 1e-05
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:27 - 		warmup_epoch: 5
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	weight_decay: 1e-06
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:22 - preprocess_conf:
+[2023-08-07 22:54:22.207167 INFO   ] utils:print_arguments:29 - 	feature_method: Fbank
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:25 - 	method_args:
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:27 - 		num_mel_bins: 80
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:27 - 		sample_frequency: 16000
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:22 - train_conf:
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:29 - 	log_interval: 10
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:29 - 	max_epoch: 30
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:31 - use_model: EcapaTdnn
+[2023-08-07 22:54:22.208167 INFO   ] utils:print_arguments:32 - ------------------------------------------------
+[2023-08-07 22:54:22.213166 WARNING] trainer:__init__:67 - Windows系统不支持多线程读取数据，已自动关闭！
+==========================================================================================
+Layer (type:depth-idx)                   Output Shape              Param #
+==========================================================================================
+EcapaTdnn                                [1, 10]                   --
+├─Conv1dReluBn: 1-1                      [1, 512, 98]              --
+│    └─Conv1d: 2-1                       [1, 512, 98]              204,800
+│    └─BatchNorm1d: 2-2                  [1, 512, 98]              1,024
+├─Sequential: 1-2                        [1, 512, 98]              --
+│    └─Conv1dReluBn: 2-3                 [1, 512, 98]              --
+│    │    └─Conv1d: 3-1                  [1, 512, 98]              262,144
+│    │    └─BatchNorm1d: 3-2             [1, 512, 98]              1,024
+│    └─Res2Conv1dReluBn: 2-4             [1, 512, 98]              --
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+│    │    └─ModuleList: 3-15             --                        (recursive)
+│    │    └─ModuleList: 3-16             --                        (recursive)
+···································
+│    │    └─ModuleList: 3-56             --                        (recursive)
+│    │    └─ModuleList: 3-55             --                        (recursive)
+│    │    └─ModuleList: 3-56             --                        (recursive)
+│    │    └─ModuleList: 3-55             --                        (recursive)
+│    │    └─ModuleList: 3-56             --                        (recursive)
+│    └─Conv1dReluBn: 2-13                [1, 512, 98]              --
+│    │    └─Conv1d: 3-57                 [1, 512, 98]              262,144
+│    │    └─BatchNorm1d: 3-58            [1, 512, 98]              1,024
+│    └─SE_Connect: 2-14                  [1, 512, 98]              --
+│    │    └─Linear: 3-59                 [1, 256]                  131,328
+│    │    └─Linear: 3-60                 [1, 512]                  131,584
+├─Conv1d: 1-5                            [1, 1536, 98]             2,360,832
+├─AttentiveStatsPool: 1-6                [1, 3072]                 --
+│    └─Conv1d: 2-15                      [1, 128, 98]              196,736
+│    └─Conv1d: 2-16                      [1, 1536, 98]             198,144
+├─BatchNorm1d: 1-7                       [1, 3072]                 6,144
+├─Linear: 1-8                            [1, 192]                  590,016
+├─BatchNorm1d: 1-9                       [1, 192]                  384
+├─Linear: 1-10                           [1, 10]                   1,930
+==========================================================================================
+Total params: 6,188,490
+Trainable params: 6,188,490
+Non-trainable params: 0
+Total mult-adds (M): 470.96
+==========================================================================================
+Input size (MB): 0.03
+Forward/backward pass size (MB): 10.28
+Params size (MB): 24.75
+Estimated Total Size (MB): 35.07
+==========================================================================================
+[2023-08-07 22:54:26.726095 INFO   ] trainer:train:344 - 训练数据：8644
+[2023-08-07 22:54:30.092504 INFO   ] trainer:__train_epoch:296 - Train epoch: [1/30], batch: [0/4], loss: 2.57033, accuracy: 0.06250, learning rate: 0.00001000, speed: 19.02 data/sec, eta: 0:06:43
+```
+
+# Eval
+
+At the end of each training round, we can perform an evaluation, which will output the accuracy. We also save the mixture matrix image, and save the path `output/images/` as follows.
+![混合矩阵](docs/images/image1.png)
+
+# Inference
+
+At the end of the training, we are given a model parameter file, and we use this model to predict the audio.
+
+```shell
+python infer.py --audio_path=dataset/UrbanSound8K/audio/fold5/156634-5-2-5.wav
+```
+
+# Other Functions
+
+ - In order to read the recorded data and make a dataset easily, we provide the recording program `record_audio.py`, which is used to record audio with a sample rate of 16,000, single channel, 16bit.
+
+```shell
+python record_audio.py
+```
+
+ - `infer_record.py`This program is used to continuously perform recording recognition, and we can roughly understand this program as recording recognition in real time. And this should allow us to do some interesting things, like put a microphone in a place where birds often come, and recognize it by recording it in real time, and once you recognize that there's a bird calling, if your dataset is powerful enough, and you have a dataset of every bird calling, then you can identify exactly which bird is calling. If the target bird is identified, the procedure is initiated, such as taking photos, etc.
+
+```shell
+python infer_record.py --record_seconds=3
+```
+
+# Reference
+
+1. https://github.com/PaddlePaddle/PaddleSpeech
+2. https://github.com/yeyupiaoling/PaddlePaddle-MobileFaceNets
+3. https://github.com/yeyupiaoling/PPASR
+4. https://github.com/alibaba-damo-academy/3D-Speaker

src/声源定位代码/audio-classification/audio-classification-platform/README.md

@@ -0,0 +1,136 @@
+# 声纹识别系统
+
+这是一个基于深度学习的声纹识别系统，包含Flask后端和Vue前端。
+
+## 功能特性
+
+- 🎵 支持多种音频格式上传识别
+- 🎤 实时录音识别功能
+- 📊 置信度可视化展示
+- 📱 响应式设计，支持移动端
+- 📝 识别历史记录
+- 📋 结果导出功能
+
+## 技术栈
+
+### 后端
+- Flask + Flask-CORS
+- PyTorch + MAClsPredictor
+- librosa + soundfile
+
+### 前端
+- Vue 3 + Composition API
+- Element Plus UI框架
+- Axios HTTP客户端
+- Vite构建工具
+
+## 快速开始
+
+### 环境要求
+
+- Python 3.8+
+- Node.js 16+
+- 已训练好的音频分类模型
+
+### 1. 安装后端依赖
+
+```bash
+cd audio-classification-platform/backend
+pip install -r requirements.txt
+```
+
+### 2. 安装前端依赖
+
+```bash
+cd audio-classification-platform/frontend
+npm install
+```
+
+### 3. 启动后端服务
+
+```bash
+cd audio-classification-platform/backend
+python app.py
+```
+
+后端服务将在 http://localhost:5000 启动
+
+### 4. 启动前端服务
+
+```bash
+cd audio-classification-platform/frontend
+npm run dev
+```
+
+前端服务将在 http://localhost:3000 启动
+
+## 使用说明
+
+1. 打开浏览器访问 http://localhost:3000
+2. 首次使用需要初始化模型（确保模型文件路径正确）
+3. 使用文件上传功能或实时录音功能进行音频识别
+4. 查看识别结果和置信度
+5. 可以查看历史识别记录
+
+## 配置说明
+
+### 后端配置
+
+在 `backend/config.py` 中可以配置：
+
+- 模型路径
+- 上传文件限制
+- GPU使用设置
+- 音频处理参数
+
+### 前端配置
+
+在 `frontend/vite.config.js` 中可以配置：
+
+- 开发服务器端口
+- 代理设置
+- 构建选项
+
+## API接口
+
+- `GET /api/health` - 健康检查
+- `POST /api/init` - 初始化模型
+- `POST /api/upload` - 上传音频文件识别
+- `POST /api/predict` - 录音数据识别
+- `GET /api/labels` - 获取分类标签
+- `GET /api/model/info` - 获取模型信息
+
+## 部署
+
+### 生产环境构建
+
+```bash
+# 构建前端
+cd frontend
+npm run build
+
+# 启动后端（推荐使用gunicorn）
+cd backend
+gunicorn -w 4 -b 0.0.0.0:5000 app:app
+```
+
+## 故障排除
+
+1. **模型初始化失败**
+   - 检查模型文件路径是否正确
+   - 确保配置文件存在
+   - 检查GPU驱动（如果使用GPU）
+
+2. **音频上传失败**
+   - 检查文件格式是否支持
+   - 检查文件大小是否超限
+   - 检查网络连接
+
+3. **录音功能不可用**
+   - 检查浏览器麦克风权限
+   - 使用HTTPS协议（录音功能需要）
+   - 检查设备麦克风是否正常
+
+## 许可证
+
+Apache License 2.0

src/声源定位代码/audio-classification/audio-classification-platform/backend/app.py

@@ -0,0 +1,350 @@
+import os
+import sys
+import tempfile
+import uuid
+from datetime import datetime
+from flask import Flask, request, jsonify, send_from_directory
+from flask_cors import CORS
+from werkzeug.utils import secure_filename
+import librosa
+import soundfile as sf
+import numpy as np
+from loguru import logger
+
+# 添加项目根目录到Python路径
+sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
+
+from macls.predict import MAClsPredictor
+
+app = Flask(__name__)
+CORS(app)  # 允许跨域请求
+
+# 配置
+app.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024  # 最大50MB
+app.config['UPLOAD_FOLDER'] = 'uploads'
+app.config['ALLOWED_EXTENSIONS'] = {'wav', 'mp3', 'flac', 'm4a', 'ogg', 'aac'}
+
+# 确保上传目录存在
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+
+# 全局预测器实例
+predictor = None
+
+def allowed_file(filename):
+    """检查文件扩展名是否被允许"""
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']
+
+def convert_audio_format(input_path, target_sample_rate=16000):
+    """将音频转换为模型需要的格式"""
+    try:
+        # 使用librosa加载音频，自动转换采样率
+        audio_data, sr = librosa.load(input_path, sr=target_sample_rate)
+        
+        # 创建临时文件
+        temp_file = tempfile.NamedTemporaryFile(suffix='.wav', delete=False)
+        temp_path = temp_file.name
+        temp_file.close()
+        
+        # 保存为wav格式
+        sf.write(temp_path, audio_data, target_sample_rate)
+        
+        return temp_path, audio_data, target_sample_rate
+    except Exception as e:
+        logger.error(f"音频格式转换失败: {str(e)}")
+        raise e
+
+@app.route('/api/health', methods=['GET'])
+def health_check():
+    """健康检查接口"""
+    return jsonify({
+        'status': 'success',
+        'message': '服务正常运行',
+        'timestamp': datetime.now().isoformat()
+    })
+
+@app.route('/api/init', methods=['POST'])
+def init_model():
+    """初始化模型"""
+    global predictor
+    try:
+        data = request.get_json()
+        
+        # 默认配置路径
+        configs = data.get('configs', '../../configs/cam++.yml')
+        model_path = data.get('model_path', '../../models/CAMPPlus_Fbank/best_model/')
+        use_gpu = data.get('use_gpu', True)
+        
+        # 转换为绝对路径
+        configs = os.path.abspath(os.path.join(os.path.dirname(__file__), configs))
+        model_path = os.path.abspath(os.path.join(os.path.dirname(__file__), model_path))
+        
+        if not os.path.exists(configs):
+            return jsonify({
+                'status': 'error',
+                'message': f'配置文件不存在: {configs}'
+            }), 400
+            
+        if not os.path.exists(model_path):
+            return jsonify({
+                'status': 'error',
+                'message': f'模型路径不存在: {model_path}'
+            }), 400
+        
+        # 初始化预测器
+        predictor = MAClsPredictor(
+            configs=configs,
+            model_path=model_path,
+            use_gpu=use_gpu
+        )
+        
+        logger.info("模型初始化成功")
+        
+        return jsonify({
+            'status': 'success',
+            'message': '模型初始化成功',
+            'config': {
+                'configs': configs,
+                'model_path': model_path,
+                'use_gpu': use_gpu
+            }
+        })
+        
+    except Exception as e:
+        logger.error(f"模型初始化失败: {str(e)}")
+        return jsonify({
+            'status': 'error',
+            'message': f'模型初始化失败: {str(e)}'
+        }), 500
+
+@app.route('/api/upload', methods=['POST'])
+def upload_and_predict():
+    """上传音频文件并进行预测"""
+    global predictor
+    
+    if predictor is None:
+        return jsonify({
+            'status': 'error',
+            'message': '模型未初始化，请先调用 /api/init 接口'
+        }), 400
+    
+    if 'file' not in request.files:
+        return jsonify({
+            'status': 'error',
+            'message': '没有上传文件'
+        }), 400
+    
+    file = request.files['file']
+    
+    if file.filename == '':
+        return jsonify({
+            'status': 'error',
+            'message': '没有选择文件'
+        }), 400
+    
+    if file and allowed_file(file.filename):
+        try:
+            # 生成唯一文件名
+            filename = secure_filename(file.filename)
+            unique_filename = f"{uuid.uuid4()}_{filename}"
+            filepath = os.path.join(app.config['UPLOAD_FOLDER'], unique_filename)
+            
+            # 保存上传的文件
+            file.save(filepath)
+            
+            # 转换音频格式
+            converted_path, audio_data, sample_rate = convert_audio_format(filepath)
+            
+            # 进行预测
+            start_time = datetime.now()
+            label, score = predictor.predict(audio_data=converted_path)
+            end_time = datetime.now()
+            
+            # 计算预测时间
+            prediction_time = (end_time - start_time).total_seconds()
+            
+            # 清理临时文件
+            if os.path.exists(converted_path):
+                os.remove(converted_path)
+              # 可选：保留原始文件或删除
+            # os.remove(filepath)
+            
+            logger.info(f"预测完成 - 文件: {filename}, 结果: {label}, 得分: {score:.4f}, 耗时: {prediction_time:.3f}s")
+            
+            return jsonify({
+                'status': 'success',
+                'result': {
+                    'predicted_class': label,  # 前端期望的字段名
+                    'confidence': float(score),  # 前端期望的字段名
+                    'label': label,  # 保持兼容性
+                    'score': float(score),  # 保持兼容性
+                    'filename': filename,
+                    'prediction_time': prediction_time,
+                    'audio_info': {
+                        'sample_rate': sample_rate,
+                        'duration': len(audio_data) / sample_rate
+                    }
+                }
+            })
+            
+        except Exception as e:
+            logger.error(f"预测失败: {str(e)}")
+            return jsonify({
+                'status': 'error',
+                'message': f'预测失败: {str(e)}'
+            }), 500
+    else:
+        return jsonify({
+            'status': 'error',
+            'message': '不支持的文件格式，支持的格式：wav, mp3, flac, m4a, ogg, aac'
+        }), 400
+
+@app.route('/api/predict', methods=['POST'])
+def predict_audio_data():
+    """直接预测音频数据（用于录音功能）"""
+    global predictor
+    
+    if predictor is None:
+        return jsonify({
+            'status': 'error',
+            'message': '模型未初始化，请先调用 /api/init 接口'
+        }), 400
+    
+    try:
+        data = request.get_json()
+        
+        if 'audio_data' not in data:
+            return jsonify({
+                'status': 'error',
+                'message': '缺少音频数据'
+            }), 400
+        
+        audio_data = np.array(data['audio_data'], dtype=np.float32)
+        sample_rate = data.get('sample_rate', 16000)
+        
+        # 创建临时文件
+        temp_file = tempfile.NamedTemporaryFile(suffix='.wav', delete=False)
+        temp_path = temp_file.name
+        temp_file.close()
+        
+        # 保存音频数据
+        sf.write(temp_path, audio_data, sample_rate)
+        
+        # 进行预测
+        start_time = datetime.now()
+        label, score = predictor.predict(audio_data=temp_path)
+        end_time = datetime.now()
+        
+        prediction_time = (end_time - start_time).total_seconds()
+          # 清理临时文件
+        os.remove(temp_path)
+        
+        logger.info(f"录音预测完成 - 结果: {label}, 得分: {score:.4f}, 耗时: {prediction_time:.3f}s")
+        
+        return jsonify({
+            'status': 'success',
+            'result': {
+                'predicted_class': label,  # 前端期望的字段名
+                'confidence': float(score),  # 前端期望的字段名
+                'label': label,  # 保持兼容性
+                'score': float(score),  # 保持兼容性
+                'prediction_time': prediction_time,
+                'audio_info': {
+                    'sample_rate': sample_rate,
+                    'duration': len(audio_data) / sample_rate
+                }
+            }
+        })
+        
+    except Exception as e:
+        logger.error(f"录音预测失败: {str(e)}")
+        return jsonify({
+            'status': 'error',
+            'message': f'录音预测失败: {str(e)}'
+        }), 500
+
+@app.route('/api/labels', methods=['GET'])
+def get_labels():
+    """获取分类标签列表"""
+    try:
+        # 读取标签文件
+        label_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../dataset/label_list.txt'))
+        
+        if not os.path.exists(label_file_path):
+            return jsonify({
+                'status': 'error',
+                'message': '标签文件不存在'
+            }), 404
+        
+        with open(label_file_path, 'r', encoding='utf-8') as f:
+            labels = [line.strip() for line in f.readlines() if line.strip()]
+        
+        return jsonify({
+            'status': 'success',
+            'labels': labels,
+            'count': len(labels)
+        })
+        
+    except Exception as e:
+        logger.error(f"读取标签失败: {str(e)}")
+        return jsonify({
+            'status': 'error',
+            'message': f'读取标签失败: {str(e)}'
+        }), 500
+
+@app.route('/api/model/info', methods=['GET'])
+def get_model_info():
+    """获取模型信息"""
+    global predictor
+    
+    if predictor is None:
+        return jsonify({
+            'status': 'error',
+            'message': '模型未初始化'
+        }), 400
+    
+    try:
+        # 获取模型配置信息
+        return jsonify({
+            'status': 'success',
+            'model_info': {
+                'initialized': True,
+                'model_type': 'AudioClassification',
+                'framework': 'PyTorch'
+            }
+        })
+        
+    except Exception as e:
+        logger.error(f"获取模型信息失败: {str(e)}")
+        return jsonify({
+            'status': 'error',
+            'message': f'获取模型信息失败: {str(e)}'
+        }), 500
+
+@app.errorhandler(413)
+def too_large(e):
+    """文件过大错误处理"""
+    return jsonify({
+        'status': 'error',
+        'message': '文件过大，最大支持50MB'
+    }), 413
+
+@app.errorhandler(404)
+def not_found(e):
+    """404错误处理"""
+    return jsonify({
+        'status': 'error',
+        'message': '接口不存在'
+    }), 404
+
+@app.errorhandler(500)
+def internal_error(e):
+    """500错误处理"""
+    return jsonify({
+        'status': 'error',
+        'message': '服务器内部错误'
+    }), 500
+
+if __name__ == '__main__':
+    logger.info("启动音频分类API服务器...")
+    app.run(host='0.0.0.0', port=5000, debug=True)

src/声源定位代码/audio-classification/audio-classification-platform/backend/config.py

@@ -0,0 +1,39 @@
+import os
+import sys
+
+# 配置
+class Config:
+    # 模型配置
+    MODEL_CONFIG_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../configs/cam++.yml'))
+    MODEL_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../models/CAMPPlus_Fbank/best_model/'))
+    LABEL_FILE_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../dataset/label_list.txt'))
+    
+    # Flask配置
+    SECRET_KEY = 'your-secret-key-here'
+    MAX_CONTENT_LENGTH = 50 * 1024 * 1024  # 50MB
+    
+    # 文件上传配置
+    UPLOAD_FOLDER = 'uploads'
+    ALLOWED_EXTENSIONS = {'wav', 'mp3', 'flac', 'm4a', 'ogg', 'aac'}
+    
+    # 音频处理配置
+    TARGET_SAMPLE_RATE = 16000
+    
+    # 日志配置
+    LOG_LEVEL = 'INFO'
+    
+    # GPU配置
+    USE_GPU = True
+
+class DevelopmentConfig(Config):
+    DEBUG = True
+    
+class ProductionConfig(Config):
+    DEBUG = False
+    
+# 根据环境变量选择配置
+config = {
+    'development': DevelopmentConfig,
+    'production': ProductionConfig,
+    'default': DevelopmentConfig
+}

src/声源定位代码/audio-classification/audio-classification-platform/backend/requirements.txt

@@ -0,0 +1,7 @@
+Flask==2.3.3
+Flask-CORS==4.0.0
+librosa==0.10.1
+soundfile==0.12.1
+numpy==1.24.3
+loguru==0.7.2
+Werkzeug==2.3.7

src/声源定位代码/audio-classification/audio-classification-platform/frontend/index.html

@@ -0,0 +1,26 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>声纹识别系统</title>
+    <style>
+      * {
+        margin: 0;
+        padding: 0;
+        box-sizing: border-box;
+      }
+      
+      body {
+        font-family: 'Microsoft YaHei', sans-serif;
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        min-height: 100vh;
+      }
+    </style>
+  </head>
+  <body>
+    <div id="app"></div>
+    <script type="module" src="/src/main.js"></script>
+  </body>
+</html>

src/声源定位代码/audio-classification/audio-classification-platform/frontend/package.json

@@ -0,0 +1,24 @@
+{
+  "name": "audio-classification-frontend",
+  "version": "1.0.0",
+  "private": true,
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview",
+    "serve": "vite preview"
+  },  "dependencies": {
+    "vue": "^3.3.4",
+    "vue-router": "^4.2.4",
+    "axios": "^1.5.0",
+    "element-plus": "^2.3.9",
+    "@element-plus/icons-vue": "^2.1.0",
+    "echarts": "^5.4.3"
+  },
+  "devDependencies": {
+    "@vitejs/plugin-vue": "^4.3.4",
+    "vite": "^4.4.9",
+    "unplugin-vue-components": "^0.25.2",
+    "unplugin-auto-import": "^0.16.6"
+  }
+}

src/声源定位代码/audio-classification/audio-classification-platform/frontend/src/App.vue

@@ -0,0 +1,106 @@
+<template>
+  <div id="app">
+    <router-view />
+  </div>
+</template>
+
+<script setup>
+// 主应用组件
+</script>
+
+<style>
+* {
+  margin: 0;
+  padding: 0;
+  box-sizing: border-box;
+}
+
+#app {
+  min-height: 100vh;
+  background: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%);
+  font-family: 'Inter', 'Helvetica Neue', Helvetica, 'PingFang SC', 'Hiragino Sans GB', 'Microsoft YaHei', '微软雅黑', Arial, sans-serif;
+  color: #333;
+  overflow-x: hidden;
+}
+
+/* 全局美化样式 */
+body {
+  margin: 0;
+  padding: 0;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+/* 自定义滚动条 */
+::-webkit-scrollbar {
+  width: 8px;
+}
+
+::-webkit-scrollbar-track {
+  background: rgba(255, 255, 255, 0.1);
+  border-radius: 4px;
+}
+
+::-webkit-scrollbar-thumb {
+  background: rgba(255, 255, 255, 0.3);
+  border-radius: 4px;
+  transition: background 0.3s ease;
+}
+
+::-webkit-scrollbar-thumb:hover {
+  background: rgba(255, 255, 255, 0.5);
+}
+
+/* 全局动画 */
+@keyframes fadeIn {
+  from {
+    opacity: 0;
+    transform: translateY(20px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+@keyframes slideInLeft {
+  from {
+    opacity: 0;
+    transform: translateX(-30px);
+  }
+  to {
+    opacity: 1;
+    transform: translateX(0);
+  }
+}
+
+@keyframes slideInRight {
+  from {
+    opacity: 0;
+    transform: translateX(30px);
+  }
+  to {
+    opacity: 1;
+    transform: translateX(0);
+  }
+}
+
+@keyframes pulse {
+  0% {
+    transform: scale(1);
+  }
+  50% {
+    transform: scale(1.05);
+  }
+  100% {
+    transform: scale(1);
+  }
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+  #app {
+    font-size: 14px;
+  }
+}
+</style>

src/声源定位代码/audio-classification/audio-classification-platform/frontend/src/components/AudioRecorder.vue

@@ -0,0 +1,690 @@
+<template>
+  <div class="audio-recorder">
+    <div class="recorder-content">
+      <!-- 录音状态显示 -->
+      <div class="recorder-status">
+        <div class="status-indicator" :class="{ 
+          active: isRecording, 
+          ready: isReady,
+          processing: isProcessing 
+        }">
+          <div class="status-bg"></div>
+          <transition name="icon-switch" mode="out-in">
+            <el-icon class="status-icon processing" v-if="isProcessing" key="processing">
+              <Loading />
+            </el-icon>
+            <el-icon class="status-icon recording" v-else-if="isRecording" key="recording">
+              <Microphone />
+            </el-icon>
+            <el-icon class="status-icon ready" v-else-if="isReady" key="ready">
+              <Microphone />
+            </el-icon>
+            <el-icon class="status-icon default" v-else key="default">
+              <MicrophoneOne />
+            </el-icon>
+          </transition>
+          
+          <!-- 录音波纹效果 -->
+          <div v-if="isRecording" class="recording-waves">
+            <div class="wave wave-1"></div>
+            <div class="wave wave-2"></div>
+            <div class="wave wave-3"></div>
+          </div>
+        </div>
+        
+        <div class="status-text">
+          <h3 class="status-title">{{ statusTitle }}</h3>
+          <p class="primary-text">{{ statusText }}</p>
+          <transition name="slide-down">
+            <p class="secondary-text" v-if="recordingTime > 0">
+              <el-icon><Timer /></el-icon>
+              录音时长: {{ formatTime(recordingTime) }}
+            </p>
+          </transition>
+        </div>
+      </div>
+
+      <!-- 录音控制按钮 -->
+      <div class="recorder-controls">
+        <transition name="button-switch" mode="out-in">
+          <el-button
+            v-if="!isRecording && !isProcessing"
+            type="primary"
+            size="large"
+            :disabled="disabled || !isReady"
+            @click="startRecording"
+            class="record-button"
+            key="start"
+          >
+            <template #icon>
+              <el-icon><Microphone /></el-icon>
+            </template>
+            <span>开始录音</span>
+            <div class="button-glow"></div>
+          </el-button>
+          
+          <el-button
+            v-else-if="isRecording"
+            type="danger"
+            size="large"
+            @click="stopRecording"
+            class="stop-button"
+            key="stop"
+          >
+            <template #icon>
+              <el-icon><VideoPause /></el-icon>
+            </template>
+            <span>停止录音</span>
+          </el-button>
+          
+          <el-button
+            v-else
+            size="large"
+            loading
+            class="processing-button"
+            key="processing"
+          >
+            <span>正在处理...</span>
+          </el-button>
+        </transition>
+      </div>
+
+      <!-- 录音设置 -->
+      <div class="recorder-settings">
+        <div class="settings-header">
+          <el-icon><Setting /></el-icon>
+          <span>录音设置</span>
+        </div>
+        <el-form :model="settings" label-width="80px" size="small" class="settings-form">
+          <el-form-item label="录音时长">
+            <el-select v-model="settings.duration" :disabled="isRecording" class="duration-select">
+              <el-option label="3秒" :value="3">
+                <div class="option-content">
+                  <span>3秒</span>
+                  <el-tag size="small" type="info">快速</el-tag>
+                </div>
+              </el-option>
+              <el-option label="5秒" :value="5">
+                <div class="option-content">
+                  <span>5秒</span>
+                  <el-tag size="small" type="success">推荐</el-tag>
+                </div>
+              </el-option>
+              <el-option label="10秒" :value="10">
+                <div class="option-content">
+                  <span>10秒</span>
+                  <el-tag size="small" type="warning">详细</el-tag>
+                </div>
+              </el-option>
+              <el-option label="15秒" :value="15" />
+              <el-option label="30秒" :value="30" />
+            </el-select>
+          </el-form-item>
+          
+          <el-form-item label="自动停止">
+            <el-switch 
+              v-model="settings.autoStop" 
+              :disabled="isRecording"
+              active-color="#52c41a"
+              inactive-color="rgba(255, 255, 255, 0.3)"
+            />
+          </el-form-item>
+        </el-form>
+      </div>
+
+      <!-- 音频可视化 -->
+      <transition name="fade">
+        <div v-if="isRecording" class="audio-visualizer">
+          <div class="visualizer-header">
+            <el-icon><DataAnalysis /></el-icon>
+            <span>实时音频波形</span>
+          </div>
+          <canvas ref="canvasRef" class="visualizer-canvas"></canvas>
+          <div class="visualizer-info">
+            <div class="info-item">
+              <span class="label">音量:</span>
+              <div class="volume-bar">
+                <div class="volume-fill" :style="{ width: `${volume}%` }"></div>
+              </div>
+            </div>
+          </div>
+        </div>
+      </transition>
+
+      <!-- 录音预览 -->
+      <transition name="slide-up">
+        <div v-if="recordedAudio" class="audio-preview">
+          <div class="preview-header">
+            <div class="preview-title">
+              <el-icon class="preview-icon"><Headphone /></el-icon>
+              <span>录音预览</span>
+            </div>
+            <div class="preview-actions">
+              <el-button type="text" size="small" @click="playRecording" class="action-button">
+                <el-icon><VideoPlay /></el-icon>
+                播放
+              </el-button>
+              <el-button type="text" size="small" @click="clearRecording" class="action-button danger">
+                <el-icon><Delete /></el-icon>
+                删除
+              </el-button>
+            </div>
+          </div>
+          
+          <div class="audio-player-container">
+            <audio
+              ref="audioPlayerRef"
+              controls
+              :src="recordedAudio.url"
+              class="audio-player"
+            >
+              您的浏览器不支持音频播放
+            </audio>
+          </div>
+            <div class="preview-info">
+            <div class="info-item">
+              <el-icon><Timer /></el-icon>
+              <span>时长: {{ formatTime(recordedAudio.duration) }}</span>
+            </div>
+            <div class="info-item">
+              <el-icon><DataBoard /></el-icon>
+              <span>大小: {{ formatFileSize(recordedAudio.size) }}</span>
+            </div>
+          </div>
+          
+          <el-button
+            type="primary"
+            @click="submitRecording"
+            :loading="isSubmitting"
+            class="submit-button"
+          >
+            识别录音
+          </el-button>
+        </div>
+      </transition>
+    </div>
+  </div>
+</template>
+
+<script setup>
+import { ref, computed, onMounted, onUnmounted } from 'vue'
+import { ElMessage } from 'element-plus'
+import { apiService } from '../utils/api'
+
+// Props
+const props = defineProps({
+  disabled: {
+    type: Boolean,
+    default: false
+  }
+})
+
+// Emits
+const emit = defineEmits(['record-success', 'record-error'])
+
+// 响应式数据
+const isRecording = ref(false)
+const isReady = ref(false)
+const isSubmitting = ref(false)
+const isProcessing = ref(false)
+const recordingTime = ref(0)
+const recordedAudio = ref(null)
+const canvasRef = ref()
+const audioPlayerRef = ref()
+const volume = ref(0)
+
+// 录音相关变量
+let mediaRecorder = null
+let audioChunks = []
+let recordingTimer = null
+let audioContext = null
+let analyser = null
+let microphone = null
+let animationId = null
+
+// 录音设置
+const settings = ref({
+  duration: 5, // 默认5秒
+  autoStop: true
+})
+
+// 计算属性
+const statusTitle = computed(() => {
+  if (isProcessing.value) return '处理中'
+  if (isRecording.value) return '录音中'
+  if (isReady.value) return '就绪'
+  return '准备中'
+})
+
+const statusText = computed(() => {
+  if (isProcessing.value) return '正在处理录音数据...'
+  if (!isReady.value) return '正在初始化麦克风...'
+  if (isRecording.value) return '正在录音中，请对着麦克风说话'
+  return '点击开始录音按钮进行音频识别'
+})
+
+// 初始化录音
+const initRecorder = async () => {
+  try {
+    const stream = await navigator.mediaDevices.getUserMedia({ 
+      audio: {
+        sampleRate: 16000,
+        channelCount: 1,
+        echoCancellation: true,
+        noiseSuppression: true
+      } 
+    })
+    
+    // 创建MediaRecorder
+    mediaRecorder = new MediaRecorder(stream, {
+      mimeType: 'audio/webm;codecs=opus'
+    })
+    
+    // 设置音频上下文用于可视化
+    audioContext = new (window.AudioContext || window.webkitAudioContext)()
+    analyser = audioContext.createAnalyser()
+    microphone = audioContext.createMediaStreamSource(stream)
+    microphone.connect(analyser)
+    
+    analyser.fftSize = 256
+    
+    // 事件监听
+    mediaRecorder.ondataavailable = (event) => {
+      if (event.data.size > 0) {
+        audioChunks.push(event.data)
+      }
+    }
+    
+    mediaRecorder.onstop = () => {
+      const audioBlob = new Blob(audioChunks, { type: 'audio/webm' })
+      createAudioPreview(audioBlob)
+      audioChunks = []
+    }
+    
+    isReady.value = true
+    ElMessage.success('麦克风初始化成功')
+    
+  } catch (error) {
+    console.error('麦克风初始化失败:', error)
+    ElMessage.error('无法访问麦克风，请检查权限设置')
+  }
+}
+
+// 开始录音
+const startRecording = () => {
+  if (!mediaRecorder || mediaRecorder.state !== 'inactive') return
+  
+  audioChunks = []
+  recordingTime.value = 0
+  isRecording.value = true
+  
+  mediaRecorder.start()
+  
+  // 开始计时
+  recordingTimer = setInterval(() => {
+    recordingTime.value++
+    
+    // 自动停止
+    if (settings.value.autoStop && recordingTime.value >= settings.value.duration) {
+      stopRecording()
+    }
+  }, 1000)
+  
+  // 开始音频可视化
+  startVisualization()
+  
+  ElMessage.info('开始录音')
+}
+
+// 停止录音
+const stopRecording = () => {
+  if (!mediaRecorder || mediaRecorder.state !== 'recording') return
+  
+  isRecording.value = false
+  mediaRecorder.stop()
+  
+  // 清除计时器
+  if (recordingTimer) {
+    clearInterval(recordingTimer)
+    recordingTimer = null
+  }
+  
+  // 停止可视化
+  stopVisualization()
+  
+  ElMessage.success('录音完成')
+}
+
+// 创建音频预览
+const createAudioPreview = (audioBlob) => {
+  const url = URL.createObjectURL(audioBlob)
+  recordedAudio.value = {
+    blob: audioBlob,
+    url: url,
+    duration: recordingTime.value,
+    size: audioBlob.size
+  }
+}
+
+// 播放录音
+const playRecording = () => {
+  if (audioPlayerRef.value) {
+    audioPlayerRef.value.play()
+  }
+}
+
+// 清除录音
+const clearRecording = () => {
+  if (recordedAudio.value) {
+    URL.revokeObjectURL(recordedAudio.value.url)
+    recordedAudio.value = null
+  }
+  recordingTime.value = 0
+}
+
+// 提交录音进行识别
+const submitRecording = async () => {
+  if (!recordedAudio.value) return
+  
+  isSubmitting.value = true
+  
+  try {
+    // 使用Web Audio API解码音频数据
+    const arrayBuffer = await recordedAudio.value.blob.arrayBuffer()
+    
+    // 创建临时音频上下文用于解码
+    const tempAudioContext = new (window.AudioContext || window.webkitAudioContext)()
+    const audioBuffer = await tempAudioContext.decodeAudioData(arrayBuffer)
+    
+    // 获取第一个声道的音频数据
+    const channelData = audioBuffer.getChannelData(0)
+    const audioData = Array.from(channelData)
+    
+    // 关闭临时音频上下文
+    await tempAudioContext.close()
+    
+    const response = await apiService.predictAudioData({
+      audio_data: audioData,
+      sample_rate: audioBuffer.sampleRate
+    })
+    
+    if (response.data.status === 'success') {
+      emit('record-success', response.data.result)
+      clearRecording()
+    } else {
+      throw new Error(response.data.message)
+    }
+    
+  } catch (error) {
+    emit('record-error', error.message)
+  } finally {
+    isSubmitting.value = false
+  }
+}
+
+// 开始音频可视化
+const startVisualization = () => {
+  if (!canvasRef.value || !analyser) return
+  
+  const canvas = canvasRef.value
+  const ctx = canvas.getContext('2d')
+  const bufferLength = analyser.frequencyBinCount
+  const dataArray = new Uint8Array(bufferLength)
+  
+  const draw = () => {
+    if (!isRecording.value) return
+    
+    animationId = requestAnimationFrame(draw)
+    
+    analyser.getByteFrequencyData(dataArray)
+    
+    ctx.fillStyle = 'rgb(255, 255, 255)'
+    ctx.fillRect(0, 0, canvas.width, canvas.height)
+    
+    const barWidth = (canvas.width / bufferLength) * 2.5
+    let barHeight
+    let x = 0
+    
+    for (let i = 0; i < bufferLength; i++) {
+      barHeight = dataArray[i] / 255 * canvas.height
+      
+      ctx.fillStyle = `rgb(${barHeight + 100}, 102, 234)`
+      ctx.fillRect(x, canvas.height - barHeight, barWidth, barHeight)
+      
+      x += barWidth + 1
+    }
+  }
+  
+  draw()
+}
+
+// 停止音频可视化
+const stopVisualization = () => {
+  if (animationId) {
+    cancelAnimationFrame(animationId)
+    animationId = null
+  }
+}
+
+// 格式化时间
+const formatTime = (seconds) => {
+  const mins = Math.floor(seconds / 60)
+  const secs = seconds % 60
+  return `${mins.toString().padStart(2, '0')}:${secs.toString().padStart(2, '0')}`
+}
+
+// 格式化文件大小
+const formatFileSize = (bytes) => {
+  if (bytes === 0) return '0 B'
+  
+  const k = 1024
+  const sizes = ['B', 'KB', 'MB', 'GB']
+  const i = Math.floor(Math.log(bytes) / Math.log(k))
+  
+  return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i]
+}
+
+// 组件挂载
+onMounted(() => {
+  initRecorder()
+})
+
+// 组件卸载
+onUnmounted(() => {
+  if (recordingTimer) {
+    clearInterval(recordingTimer)
+  }
+  
+  if (animationId) {
+    cancelAnimationFrame(animationId)
+  }
+  
+  if (mediaRecorder && mediaRecorder.stream) {
+    mediaRecorder.stream.getTracks().forEach(track => track.stop())
+  }
+  
+  if (audioContext && audioContext.state !== 'closed') {
+    audioContext.close()
+  }
+  
+  clearRecording()
+})
+</script>
+
+<style scoped>
+.audio-recorder {
+  width: 100%;
+}
+
+.recorder-content {
+  text-align: center;
+}
+
+.recorder-status {
+  margin-bottom: 30px;
+}
+
+.status-indicator {
+  width: 80px;
+  height: 80px;
+  border-radius: 50%;
+  border: 3px solid #e4e7ed;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  margin: 0 auto 15px;
+  transition: all 0.3s ease;
+  background: #f5f5f5;
+}
+
+.status-indicator.ready {
+  border-color: #67c23a;
+  background: #f0f9ff;
+}
+
+.status-indicator.active {
+  border-color: #e6a23c;
+  background: #fef0e6;
+  animation: pulse 2s infinite;
+}
+
+@keyframes pulse {
+  0% {
+    transform: scale(1);
+    box-shadow: 0 0 0 0 rgba(230, 162, 60, 0.7);
+  }
+  70% {
+    transform: scale(1.05);
+    box-shadow: 0 0 0 10px rgba(230, 162, 60, 0);
+  }
+  100% {
+    transform: scale(1);
+    box-shadow: 0 0 0 0 rgba(230, 162, 60, 0);
+  }
+}
+
+.status-icon {
+  font-size: 2.5rem;
+  color: #999;
+}
+
+.status-indicator.ready .status-icon {
+  color: #67c23a;
+}
+
+.status-indicator.active .status-icon {
+  color: #e6a23c;
+}
+
+.status-text {
+  text-align: center;
+}
+
+.primary-text {
+  font-size: 1.1rem;
+  font-weight: 500;
+  color: #333;
+  margin-bottom: 5px;
+}
+
+.secondary-text {
+  font-size: 0.9rem;
+  color: #666;
+}
+
+.recorder-controls {
+  margin-bottom: 25px;
+}
+
+.record-button,
+.stop-button {
+  min-width: 120px;
+  height: 45px;
+  font-size: 1rem;
+}
+
+.recorder-settings {
+  margin-bottom: 25px;
+  padding: 15px;
+  background: #f9f9f9;
+  border-radius: 8px;
+}
+
+.audio-visualizer {
+  margin-bottom: 25px;
+  padding: 15px;
+  background: #f0f0f0;
+  border-radius: 8px;
+}
+
+.visualizer-canvas {
+  width: 100%;
+  height: 100px;
+  border-radius: 4px;
+  background: white;
+}
+
+.audio-preview {
+  margin-top: 25px;
+  padding: 20px;
+  border: 1px solid #e4e7ed;
+  border-radius: 8px;
+  background: #f9f9f9;
+}
+
+.preview-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  margin-bottom: 15px;
+  font-weight: 500;
+  color: #333;
+}
+
+.preview-header span {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+}
+
+.preview-actions {
+  display: flex;
+  gap: 10px;
+}
+
+.audio-player {
+  width: 100%;
+  margin-bottom: 15px;
+}
+
+.preview-info {
+  display: flex;
+  justify-content: space-between;
+  font-size: 0.9rem;
+  color: #666;
+  margin-bottom: 15px;
+}
+
+.submit-button {
+  width: 100%;
+}
+
+/* 响应式设计 */
+@media (max-width: 480px) {
+  .status-indicator {
+    width: 60px;
+    height: 60px;
+  }
+  
+  .status-icon {
+    font-size: 2rem;
+  }
+  
+  .record-button,
+  .stop-button {
+    min-width: 100px;
+    height: 40px;
+    font-size: 0.9rem;
+  }
+}
+</style>

src/声源定位代码/audio-classification/audio-classification-platform/frontend/src/components/AudioUpload.vue

@@ -0,0 +1,714 @@
+<template>
+  <div class="audio-upload">
+    <el-upload
+      ref="uploadRef"
+      class="upload-dragger"
+      drag
+      :action="uploadUrl"
+      :headers="uploadHeaders"
+      :data="uploadData"
+      :on-success="handleSuccess"
+      :on-error="handleError"
+      :on-progress="handleProgress"
+      :before-upload="beforeUpload"
+      :disabled="disabled"
+      accept="audio/*,.wav,.mp3,.flac,.m4a,.ogg,.aac"
+      :show-file-list="false"
+    >
+      <div class="upload-content">
+        <!-- 上传图标和动画 -->
+        <div class="upload-icon-wrapper">
+          <transition name="icon-switch" mode="out-in">
+            <el-icon class="upload-icon success" v-if="uploadSuccess" key="success">
+              <Check />
+            </el-icon>
+            <el-icon class="upload-icon uploading" v-else-if="uploading" key="uploading">
+              <Loading />
+            </el-icon>
+            <el-icon class="upload-icon default" v-else key="default">
+              <Upload />
+            </el-icon>
+          </transition>
+          <div class="icon-glow" :class="{ active: uploading || uploadSuccess }"></div>
+        </div>
+        
+        <!-- 上传文本 -->
+        <div class="upload-text">
+          <transition name="fade" mode="out-in">
+            <div v-if="uploadSuccess" key="success" class="success-message">
+              <p class="primary-text success">✓ 文件上传成功</p>
+              <p class="secondary-text">正在进行音频识别...</p>
+            </div>
+            <div v-else-if="uploading" key="uploading" class="uploading-message">
+              <p class="primary-text uploading">正在上传并识别中...</p>
+              <p class="secondary-text">请稍等，模型正在分析您的音频</p>
+            </div>
+            <div v-else key="default" class="default-message">
+              <p class="primary-text">
+                <span class="highlight">点击</span> 或 <span class="highlight">拖拽</span> 音频文件到此处
+              </p>
+              <p class="secondary-text">
+                支持 <span class="format-tag">WAV</span>、<span class="format-tag">MP3</span>、<span class="format-tag">FLAC</span>、<span class="format-tag">M4A</span> 等格式
+              </p>
+              <p class="secondary-text size-limit">
+                文件大小限制：<span class="limit-value">50MB</span>
+              </p>
+            </div>
+          </transition>
+        </div>
+        
+        <!-- 上传进度 -->
+        <transition name="slide-down">
+          <div v-if="uploading" class="progress-container">
+            <div class="progress-wrapper">
+              <el-progress
+                :percentage="uploadProgress"
+                :stroke-width="8"
+                :show-text="false"
+                :color="progressColor"
+                class="custom-progress"
+              />
+              <div class="progress-indicator">
+                <span class="progress-text">{{ uploadProgress }}%</span>
+                <div class="progress-dots">
+                  <span class="dot"></span>
+                  <span class="dot"></span>
+                  <span class="dot"></span>
+                </div>
+              </div>
+            </div>
+          </div>
+        </transition>
+      </div>
+    </el-upload>
+
+    <!-- 音频预览 -->
+    <transition name="slide-up">
+      <div v-if="audioPreview" class="audio-preview">
+        <div class="preview-header">
+          <div class="preview-title">
+            <el-icon class="preview-icon"><Headphone /></el-icon>
+            <span>音频预览</span>
+          </div>
+          <el-button type="text" size="small" @click="clearPreview" class="close-button">
+            <el-icon><Close /></el-icon>
+          </el-button>
+        </div>
+        
+        <div class="audio-player-container">
+          <audio
+            ref="audioPlayerRef"
+            controls
+            :src="audioPreview.url"
+            class="audio-player"
+          >
+            您的浏览器不支持音频播放
+          </audio>
+        </div>
+        
+        <div class="audio-info">
+          <div class="info-item">
+            <el-icon><Document /></el-icon>
+            <span>{{ audioPreview.name }}</span>
+          </div>
+          <div class="info-item">
+            <el-icon><DataBoard /></el-icon>
+            <span>{{ formatFileSize(audioPreview.size) }}</span>
+          </div>
+        </div>
+      </div>
+    </transition>
+  </div>
+</template>
+
+<script setup>
+import { ref, computed } from 'vue'
+import { ElMessage } from 'element-plus'
+
+// Props
+const props = defineProps({
+  disabled: {
+    type: Boolean,
+    default: false
+  }
+})
+
+// Emits
+const emit = defineEmits(['upload-success', 'upload-error'])
+
+// 响应式数据
+const uploadRef = ref()
+const audioPlayerRef = ref()
+const uploading = ref(false)
+const uploadSuccess = ref(false)
+const uploadProgress = ref(0)
+const audioPreview = ref(null)
+
+// 计算属性
+const progressColor = computed(() => {
+  if (uploadProgress.value < 30) return '#409eff'
+  if (uploadProgress.value < 70) return '#e6a23c'
+  return '#67c23a'
+})
+
+// 上传配置
+const uploadUrl = '/api/upload'
+const uploadHeaders = {
+  // 不要手动设置 Content-Type，让浏览器自动设置 boundary
+}
+const uploadData = {}
+
+// 上传前检查
+const beforeUpload = (file) => {
+  // 检查文件类型
+  const allowedTypes = ['audio/wav', 'audio/mpeg', 'audio/flac', 'audio/m4a', 'audio/ogg', 'audio/aac']
+  const fileExtension = file.name.split('.').pop().toLowerCase()
+  const allowedExtensions = ['wav', 'mp3', 'flac', 'm4a', 'ogg', 'aac']
+  
+  if (!allowedTypes.includes(file.type) && !allowedExtensions.includes(fileExtension)) {
+    ElMessage.error('不支持的文件格式，请上传音频文件')
+    return false
+  }
+  
+  // 检查文件大小 (50MB)
+  const maxSize = 50 * 1024 * 1024
+  if (file.size > maxSize) {
+    ElMessage.error('文件大小不能超过50MB')
+    return false
+  }
+  
+  // 创建音频预览
+  createAudioPreview(file)
+  
+  uploading.value = true
+  uploadProgress.value = 0
+  
+  return true
+}
+
+// 创建音频预览
+const createAudioPreview = (file) => {
+  const url = URL.createObjectURL(file)
+  audioPreview.value = {
+    name: file.name,
+    size: file.size,
+    url: url
+  }
+}
+
+// 清除预览
+const clearPreview = () => {
+  if (audioPreview.value) {
+    URL.revokeObjectURL(audioPreview.value.url)
+    audioPreview.value = null
+  }
+}
+
+// 上传进度
+const handleProgress = (event) => {
+  uploadProgress.value = Math.round(event.percent)
+}
+
+// 上传成功
+const handleSuccess = (response, file) => {
+  uploading.value = false
+  uploadProgress.value = 100
+  
+  if (response.status === 'success') {
+    emit('upload-success', response.result, file.name)
+    ElMessage.success('音频上传并识别成功')
+  } else {
+    emit('upload-error', response.message)
+    ElMessage.error(`识别失败: ${response.message}`)
+  }
+}
+
+// 上传失败
+const handleError = (error, file) => {
+  uploading.value = false
+  uploadProgress.value = 0
+  
+  let errorMessage = '上传失败'
+  try {
+    const errorData = JSON.parse(error.message)
+    errorMessage = errorData.message || errorMessage
+  } catch (e) {
+    errorMessage = error.message || errorMessage
+  }
+  
+  emit('upload-error', errorMessage)
+  ElMessage.error(`上传失败: ${errorMessage}`)
+}
+
+// 格式化文件大小
+const formatFileSize = (bytes) => {
+  if (bytes === 0) return '0 B'
+  
+  const k = 1024
+  const sizes = ['B', 'KB', 'MB', 'GB']
+  const i = Math.floor(Math.log(bytes) / Math.log(k))
+  
+  return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i]
+}
+</script>
+
+<style scoped>
+.audio-upload {
+  width: 100%;
+}
+
+.upload-dragger {
+  width: 100%;
+}
+
+:deep(.el-upload) {
+  width: 100%;
+}
+
+:deep(.el-upload-dragger) {
+  width: 100%;
+  height: 280px;
+  border: 2px dashed rgba(255, 255, 255, 0.3);
+  border-radius: 20px;
+  background: rgba(255, 255, 255, 0.05);
+  backdrop-filter: blur(10px);
+  transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
+  position: relative;
+  overflow: hidden;
+}
+
+:deep(.el-upload-dragger:hover) {
+  border-color: rgba(255, 255, 255, 0.6);
+  background: rgba(255, 255, 255, 0.1);
+  transform: translateY(-2px);
+  box-shadow: 0 10px 30px rgba(0, 0, 0, 0.2);
+}
+
+:deep(.el-upload-dragger.is-dragover) {
+  border-color: #52c41a;
+  background: rgba(82, 196, 26, 0.1);
+  transform: scale(1.02);
+}
+
+.upload-content {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  height: 100%;
+  padding: 30px;
+  position: relative;
+}
+
+/* 图标样式 */
+.upload-icon-wrapper {
+  position: relative;
+  margin-bottom: 25px;
+}
+
+.upload-icon {
+  font-size: 4rem;
+  transition: all 0.4s ease;
+  display: block;
+}
+
+.upload-icon.default {
+  color: rgba(255, 255, 255, 0.6);
+}
+
+.upload-icon.uploading {
+  color: #409eff;
+  animation: rotating 2s linear infinite;
+}
+
+.upload-icon.success {
+  color: #52c41a;
+  animation: bounce 0.6s ease;
+}
+
+.icon-glow {
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%);
+  width: 100px;
+  height: 100px;
+  border-radius: 50%;
+  background: radial-gradient(circle, rgba(255, 255, 255, 0.2) 0%, transparent 70%);
+  opacity: 0;
+  transition: opacity 0.3s ease;
+}
+
+.icon-glow.active {
+  opacity: 1;
+  animation: pulse 2s ease-in-out infinite;
+}
+
+/* 动画 */
+@keyframes rotating {
+  0% { transform: rotate(0deg); }
+  100% { transform: rotate(360deg); }
+}
+
+@keyframes bounce {
+  0%, 20%, 53%, 80%, 100% { transform: scale(1); }
+  40%, 43% { transform: scale(1.2); }
+  70% { transform: scale(1.1); }
+}
+
+@keyframes pulse {
+  0%, 100% { opacity: 0.6; transform: translate(-50%, -50%) scale(1); }
+  50% { opacity: 1; transform: translate(-50%, -50%) scale(1.1); }
+}
+
+/* 文本样式 */
+.upload-text {
+  text-align: center;
+  width: 100%;
+}
+
+.primary-text {
+  font-size: 1.3rem;
+  font-weight: 600;
+  color: white;
+  margin-bottom: 12px;
+  line-height: 1.4;
+}
+
+.primary-text.uploading {
+  color: #409eff;
+}
+
+.primary-text.success {
+  color: #52c41a;
+}
+
+.highlight {
+  color: #409eff;
+  font-weight: 700;
+  text-shadow: 0 0 10px rgba(64, 158, 255, 0.3);
+}
+
+.secondary-text {
+  font-size: 1rem;
+  color: rgba(255, 255, 255, 0.7);
+  margin-bottom: 8px;
+  line-height: 1.3;
+}
+
+.format-tag {
+  display: inline-block;
+  padding: 2px 8px;
+  background: rgba(255, 255, 255, 0.2);
+  border-radius: 12px;
+  font-size: 0.85rem;
+  font-weight: 500;
+  margin: 0 2px;
+}
+
+.size-limit {
+  font-size: 0.9rem;
+}
+
+.limit-value {
+  color: #409eff;
+  font-weight: 600;
+}
+
+/* 进度条样式 */
+.progress-container {
+  width: 100%;
+  max-width: 350px;
+  margin-top: 25px;
+}
+
+.progress-wrapper {
+  position: relative;
+}
+
+.custom-progress {
+  margin-bottom: 15px;
+}
+
+:deep(.el-progress-bar__outer) {
+  background: rgba(255, 255, 255, 0.2);
+  border-radius: 20px;
+  overflow: hidden;
+}
+
+:deep(.el-progress-bar__inner) {
+  border-radius: 20px;
+  background: linear-gradient(90deg, #409eff 0%, #52c41a 100%);
+  transition: all 0.3s ease;
+}
+
+.progress-indicator {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+}
+
+.progress-text {
+  font-size: 1rem;
+  font-weight: 600;
+  color: #409eff;
+}
+
+.progress-dots {
+  display: flex;
+  gap: 4px;
+}
+
+.dot {
+  width: 6px;
+  height: 6px;
+  border-radius: 50%;
+  background: #409eff;
+  animation: dot-pulse 1.5s ease-in-out infinite;
+}
+
+.dot:nth-child(2) {
+  animation-delay: 0.2s;
+}
+
+.dot:nth-child(3) {
+  animation-delay: 0.4s;
+}
+
+@keyframes dot-pulse {
+  0%, 60%, 100% {
+    opacity: 0.3;
+    transform: scale(1);
+  }
+  30% {
+    opacity: 1;
+    transform: scale(1.3);
+  }
+}
+
+/* 音频预览样式 */
+.audio-preview {
+  margin-top: 25px;
+  padding: 25px;
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  border-radius: 16px;
+  background: rgba(255, 255, 255, 0.1);
+  backdrop-filter: blur(10px);
+  transition: all 0.3s ease;
+}
+
+.audio-preview:hover {
+  background: rgba(255, 255, 255, 0.15);
+  transform: translateY(-2px);
+}
+
+.preview-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  margin-bottom: 20px;
+}
+
+.preview-title {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  font-weight: 600;
+  color: white;
+  font-size: 1.1rem;
+}
+
+.preview-icon {
+  color: #409eff;
+  font-size: 1.2rem;
+}
+
+.close-button {
+  color: rgba(255, 255, 255, 0.6);
+  transition: all 0.3s ease;
+  border-radius: 50%;
+  width: 32px;
+  height: 32px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.close-button:hover {
+  color: #ff4d4f;
+  background: rgba(255, 77, 79, 0.1);
+  transform: scale(1.1);
+}
+
+.audio-player-container {
+  margin-bottom: 20px;
+  padding: 15px;
+  background: rgba(255, 255, 255, 0.05);
+  border-radius: 12px;
+}
+
+.audio-player {
+  width: 100%;
+  height: 40px;
+  border-radius: 8px;
+  background: rgba(255, 255, 255, 0.1);
+}
+
+:deep(.audio-player::-webkit-media-controls-panel) {
+  background: rgba(255, 255, 255, 0.1);
+}
+
+.audio-info {
+  display: flex;
+  justify-content: space-between;
+  gap: 20px;
+}
+
+.info-item {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  font-size: 0.95rem;
+  color: rgba(255, 255, 255, 0.8);
+  padding: 8px 12px;
+  background: rgba(255, 255, 255, 0.1);
+  border-radius: 20px;
+  flex: 1;
+  justify-content: center;
+}
+
+.info-item .el-icon {
+  color: #409eff;
+}
+
+/* 过渡动画 */
+.icon-switch-enter-active,
+.icon-switch-leave-active {
+  transition: all 0.3s ease;
+}
+
+.icon-switch-enter-from {
+  opacity: 0;
+  transform: scale(0.8) rotate(90deg);
+}
+
+.icon-switch-leave-to {
+  opacity: 0;
+  transform: scale(0.8) rotate(-90deg);
+}
+
+.fade-enter-active,
+.fade-leave-active {
+  transition: all 0.4s ease;
+}
+
+.fade-enter-from,
+.fade-leave-to {
+  opacity: 0;
+  transform: translateY(10px);
+}
+
+.slide-down-enter-active,
+.slide-down-leave-active {
+  transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.slide-down-enter-from {
+  opacity: 0;
+  transform: translateY(-20px);
+  max-height: 0;
+}
+
+.slide-down-leave-to {
+  opacity: 0;
+  transform: translateY(-20px);
+  max-height: 0;
+}
+
+.slide-up-enter-active,
+.slide-up-leave-active {
+  transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.slide-up-enter-from {
+  opacity: 0;
+  transform: translateY(20px);
+}
+
+.slide-up-leave-to {
+  opacity: 0;
+  transform: translateY(-20px);
+}
+
+/* 禁用状态样式 */
+:deep(.el-upload.is-disabled .el-upload-dragger) {
+  background: rgba(255, 255, 255, 0.02);
+  border-color: rgba(255, 255, 255, 0.1);
+  cursor: not-allowed;
+}
+
+:deep(.el-upload.is-disabled .upload-icon) {
+  color: rgba(255, 255, 255, 0.3);
+}
+
+:deep(.el-upload.is-disabled .primary-text) {
+  color: rgba(255, 255, 255, 0.4);
+}
+
+:deep(.el-upload.is-disabled .secondary-text) {
+  color: rgba(255, 255, 255, 0.3);
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+  :deep(.el-upload-dragger) {
+    height: 240px;
+  }
+  
+  .upload-content {
+    padding: 20px;
+  }
+  
+  .upload-icon {
+    font-size: 3rem;
+  }
+  
+  .primary-text {
+    font-size: 1.1rem;
+  }
+  
+  .secondary-text {
+    font-size: 0.9rem;
+  }
+  
+  .audio-info {
+    flex-direction: column;
+    gap: 10px;
+  }
+}
+
+@media (max-width: 480px) {
+  :deep(.el-upload-dragger) {
+    height: 200px;
+  }
+  
+  .upload-content {
+    padding: 15px;
+  }
+  
+  .upload-icon {
+    font-size: 2.5rem;
+  }
+  
+  .primary-text {
+    font-size: 1rem;
+  }
+  
+  .secondary-text {
+    font-size: 0.85rem;
+  }
+  
+  .audio-preview {
+    padding: 20px;
+  }
+}</style>

src/声源定位代码/audio-classification/audio-classification-platform/frontend/src/components/HistoryList.vue

@@ -0,0 +1,616 @@
+<template>
+  <div class="history-list">
+    <div v-if="history.length === 0" class="empty-state">
+      <el-icon class="empty-icon"><Document /></el-icon>
+      <p>暂无识别历史</p>
+    </div>
+      <div v-else class="history-items">
+      <div
+        v-for="(item, index) in paginatedHistory"
+        :key="index"
+        class="history-item"
+        @click="selectItem(item)"
+      >        <div class="item-header">
+          <div class="item-title">
+            <el-tag 
+              :type="getResultType(item.confidence || item.score)" 
+              size="small"
+              class="result-tag"
+            >
+              {{ item.predicted_class || item.label }}
+            </el-tag>
+            <span class="confidence">
+              {{ ((item.confidence || item.score) * 100).toFixed(1) }}%
+            </span>
+          </div>
+          
+          <div class="item-meta">
+            <span class="source-badge" :class="item.source">
+              <el-icon>
+                <Upload v-if="item.source === 'upload'" />
+                <Microphone v-else />
+              </el-icon>
+              {{ item.source === 'upload' ? '上传' : '录音' }}
+            </span>
+            <span class="timestamp">{{ formatTime(item.timestamp) }}</span>
+          </div>
+        </div>
+        
+        <div class="item-details">
+          <div v-if="item.filename" class="detail-row">
+            <el-icon><Document /></el-icon>
+            <span>{{ truncateFilename(item.filename) }}</span>
+          </div>
+          
+          <div v-if="item.audio_info" class="detail-row">
+            <el-icon><Timer /></el-icon>
+            <span>{{ formatDuration(item.audio_info.duration) }}</span>
+          </div>
+          
+          <div class="detail-row">
+            <el-icon><Cpu /></el-icon>
+            <span>{{ item.prediction_time?.toFixed(3) }}s</span>
+          </div>
+        </div>
+          <!-- 置信度进度条 -->
+        <div class="confidence-bar">
+          <div 
+            class="confidence-fill" 
+            :style="{ 
+              width: `${(item.confidence || item.score) * 100}%`,
+              backgroundColor: getConfidenceColor(item.confidence || item.score)
+            }"
+          ></div>
+        </div>
+      </div>
+    </div>
+      <!-- 分页（如果历史记录很多） -->
+    <div v-if="history.length > pageSize" class="pagination">
+      <el-pagination
+        :current-page="currentPage"
+        :page-size="pageSize"
+        :total="history.length"
+        layout="prev, pager, next"
+        @current-change="handlePageChange"
+        small
+      />
+    </div>
+  </div>
+</template>
+
+<script setup>
+import { ref, computed } from 'vue'
+import { 
+  Document, 
+  Upload, 
+  Microphone, 
+  Timer, 
+  Cpu 
+} from '@element-plus/icons-vue'
+
+// Props
+const props = defineProps({
+  history: {
+    type: Array,
+    default: () => []
+  }
+})
+
+// Emits
+const emit = defineEmits(['select-item'])
+
+// 响应式数据
+const currentPage = ref(1)
+const pageSize = ref(10)
+
+// 计算当前页显示的历史记录
+const paginatedHistory = computed(() => {
+  const start = (currentPage.value - 1) * pageSize.value
+  const end = start + pageSize.value
+  return props.history.slice(start, end)
+})
+
+// 获取结果类型
+const getResultType = (score) => {
+  if (score >= 0.8) return 'success'
+  if (score >= 0.6) return 'warning'
+  return 'danger'
+}
+
+// 获取置信度颜色
+const getConfidenceColor = (score) => {
+  if (score >= 0.8) return '#67c23a'
+  if (score >= 0.6) return '#e6a23c'
+  return '#f56c6c'
+}
+
+// 格式化时间
+const formatTime = (timestamp) => {
+  const date = new Date(timestamp)
+  const now = new Date()
+  const diffInSeconds = Math.floor((now - date) / 1000)
+  
+  if (diffInSeconds < 60) {
+    return '刚刚'
+  } else if (diffInSeconds < 3600) {
+    return `${Math.floor(diffInSeconds / 60)}分钟前`
+  } else if (diffInSeconds < 86400) {
+    return `${Math.floor(diffInSeconds / 3600)}小时前`
+  } else {
+    return date.toLocaleDateString() + ' ' + date.toLocaleTimeString().slice(0, 5)
+  }
+}
+
+// 格式化时长
+const formatDuration = (seconds) => {
+  if (!seconds) return 'N/A'
+  
+  if (seconds < 60) {
+    return `${seconds.toFixed(1)}s`
+  }
+  
+  const minutes = Math.floor(seconds / 60)
+  const remainingSeconds = seconds % 60
+  return `${minutes}m${remainingSeconds.toFixed(1)}s`
+}
+
+// 截断文件名
+const truncateFilename = (filename, maxLength = 20) => {
+  if (!filename || filename.length <= maxLength) return filename
+  
+  const extension = filename.split('.').pop()
+  const nameWithoutExt = filename.slice(0, -(extension.length + 1))
+  const truncatedName = nameWithoutExt.slice(0, maxLength - extension.length - 4) + '...'
+  
+  return truncatedName + '.' + extension
+}
+
+// 选择历史记录项
+const selectItem = (item) => {
+  emit('select-item', item)
+}
+
+// 分页变化处理
+const handlePageChange = (page) => {
+  currentPage.value = page
+}
+</script>
+
+<style scoped>
+/* 历史列表主容器 */
+.history-list {
+  width: 100%;
+  animation: fadeIn 0.6s ease-out;
+  position: relative;
+}
+
+/* 空状态 */
+.empty-state {
+  text-align: center;
+  padding: 80px 30px;
+  background: rgba(255, 255, 255, 0.08);
+  backdrop-filter: blur(25px);
+  border: 1px solid rgba(255, 255, 255, 0.15);
+  border-radius: 24px;
+  animation: fadeInUp 0.6s ease-out;
+  box-shadow: 
+    0 8px 32px rgba(0, 0, 0, 0.1),
+    inset 0 1px 0 rgba(255, 255, 255, 0.2);
+  position: relative;
+  overflow: hidden;
+}
+
+.empty-state::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: linear-gradient(135deg, rgba(255, 255, 255, 0.1) 0%, transparent 50%, rgba(255, 255, 255, 0.05) 100%);
+  pointer-events: none;
+}
+
+.empty-icon {
+  font-size: 5rem;
+  color: rgba(255, 255, 255, 0.5);
+  margin-bottom: 25px;
+  animation: float 3s ease-in-out infinite;
+  filter: drop-shadow(0 4px 15px rgba(255, 255, 255, 0.2));
+}
+
+.empty-state p {
+  font-size: 1.2rem;
+  margin: 0;
+  color: rgba(255, 255, 255, 0.8);
+  font-weight: 500;
+}
+
+/* 历史项目列表 */
+.history-items {
+  display: flex;
+  flex-direction: column;
+  gap: 20px;
+}
+
+/* 历史项目卡片 */
+.history-item {
+  padding: 25px;
+  background: rgba(255, 255, 255, 0.12);
+  backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  border-radius: 16px;
+  cursor: pointer;
+  transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+  position: relative;
+  overflow: hidden;
+  animation: slideInLeft 0.6s ease-out;
+  animation-fill-mode: both;
+}
+
+.history-item:nth-child(even) {
+  animation-delay: 0.1s;
+}
+
+.history-item:nth-child(odd) {
+  animation-delay: 0.2s;
+}
+
+.history-item::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: -100%;
+  width: 100%;
+  height: 100%;
+  background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.1), transparent);
+  transition: left 0.5s ease;
+}
+
+.history-item:hover::before {
+  left: 100%;
+}
+
+.history-item:hover {
+  background: rgba(255, 255, 255, 0.18);
+  border-color: rgba(102, 126, 234, 0.5);
+  transform: translateY(-5px) scale(1.02);
+  box-shadow: 0 15px 40px rgba(102, 126, 234, 0.2);
+}
+
+/* 项目头部 */
+.item-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: flex-start;
+  margin-bottom: 20px;
+}
+
+.item-title {
+  display: flex;
+  align-items: center;
+  gap: 15px;
+}
+
+.result-tag {
+  font-weight: 600 !important;
+  border-radius: 12px !important;
+  padding: 6px 14px !important;
+  border: none !important;
+  backdrop-filter: blur(10px);
+  box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
+}
+
+.result-tag.el-tag--success {
+  background: rgba(82, 196, 26, 0.2) !important;
+  color: #52c41a !important;
+  border: 1px solid rgba(82, 196, 26, 0.3) !important;
+}
+
+.result-tag.el-tag--warning {
+  background: rgba(250, 219, 20, 0.2) !important;
+  color: #fadb14 !important;
+  border: 1px solid rgba(250, 219, 20, 0.3) !important;
+}
+
+.result-tag.el-tag--danger {
+  background: rgba(255, 77, 79, 0.2) !important;
+  color: #ff4d4f !important;
+  border: 1px solid rgba(255, 77, 79, 0.3) !important;
+}
+
+.confidence {
+  font-size: 1rem;
+  font-weight: 700;
+  color: white;
+  background: rgba(64, 158, 255, 0.2);
+  padding: 6px 12px;
+  border-radius: 20px;
+  backdrop-filter: blur(10px);
+  border: 1px solid rgba(64, 158, 255, 0.3);
+}
+
+/* 项目元数据 */
+.item-meta {
+  display: flex;
+  flex-direction: column;
+  align-items: flex-end;
+  gap: 8px;
+}
+
+.source-badge {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  padding: 6px 12px;
+  border-radius: 20px;
+  font-size: 0.85rem;
+  font-weight: 600;
+  backdrop-filter: blur(10px);
+  border: 1px solid;
+  transition: all 0.3s ease;
+}
+
+.source-badge:hover {
+  transform: scale(1.05);
+}
+
+.source-badge.upload {
+  background: rgba(82, 196, 26, 0.15);
+  color: #52c41a;
+  border-color: rgba(82, 196, 26, 0.3);
+}
+
+.source-badge.record {
+  background: rgba(250, 219, 20, 0.15);
+  color: #fadb14;
+  border-color: rgba(250, 219, 20, 0.3);
+}
+
+.timestamp {
+  font-size: 0.85rem;
+  color: rgba(255, 255, 255, 0.7);
+  font-weight: 500;
+}
+
+/* 项目详情 */
+.item-details {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 20px;
+  margin-bottom: 15px;
+}
+
+.detail-row {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  font-size: 0.9rem;
+  color: rgba(255, 255, 255, 0.8);
+  background: rgba(255, 255, 255, 0.1);
+  padding: 8px 14px;
+  border-radius: 20px;
+  backdrop-filter: blur(10px);
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  transition: all 0.3s ease;
+}
+
+.detail-row:hover {
+  background: rgba(255, 255, 255, 0.15);
+  transform: translateY(-2px);
+}
+
+.detail-row .el-icon {
+  color: #409eff;
+  font-size: 1rem;
+}
+
+/* 置信度进度条 */
+.confidence-bar {
+  width: 100%;
+  height: 8px;
+  background: rgba(255, 255, 255, 0.1);
+  border-radius: 4px;
+  overflow: hidden;
+  position: relative;
+}
+
+.confidence-bar::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: rgba(255, 255, 255, 0.05);
+  border-radius: 4px;
+}
+
+.confidence-fill {
+  height: 100%;
+  border-radius: 4px;
+  transition: all 0.8s cubic-bezier(0.4, 0, 0.2, 1);
+  position: relative;
+  background: linear-gradient(90deg, #52c41a, #73d13d);
+  box-shadow: 0 0 15px rgba(82, 196, 26, 0.3);
+  animation: progressGlow 2s ease-in-out infinite alternate;
+}
+
+.confidence-fill::after {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.3), transparent);
+  animation: shimmer 2s ease-in-out infinite;
+}
+
+/* 分页 */
+.pagination {
+  margin-top: 30px;
+  display: flex;
+  justify-content: center;
+  padding: 20px;
+  background: rgba(255, 255, 255, 0.08);
+  border-radius: 16px;
+  backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.15);
+}
+
+/* 自定义分页样式 */
+:deep(.el-pagination) {
+  --el-pagination-bg-color: rgba(255, 255, 255, 0.1);
+  --el-pagination-text-color: white;
+  --el-pagination-border-radius: 8px;
+}
+
+:deep(.el-pagination .btn-prev),
+:deep(.el-pagination .btn-next),
+:deep(.el-pagination .el-pager li) {
+  background: rgba(255, 255, 255, 0.1) !important;
+  border: 1px solid rgba(255, 255, 255, 0.2) !important;
+  color: white !important;
+  backdrop-filter: blur(10px);
+  transition: all 0.3s ease;
+}
+
+:deep(.el-pagination .btn-prev:hover),
+:deep(.el-pagination .btn-next:hover),
+:deep(.el-pagination .el-pager li:hover) {
+  background: rgba(255, 255, 255, 0.2) !important;
+  transform: translateY(-2px);
+}
+
+:deep(.el-pagination .el-pager li.is-active) {
+  background: rgba(64, 158, 255, 0.3) !important;
+  border-color: rgba(64, 158, 255, 0.5) !important;
+  color: white !important;
+  box-shadow: 0 0 15px rgba(64, 158, 255, 0.4);
+}
+
+/* 动画 */
+@keyframes fadeIn {
+  from { opacity: 0; }
+  to { opacity: 1; }
+}
+
+@keyframes fadeInUp {
+  from {
+    opacity: 0;
+    transform: translateY(30px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+@keyframes slideInLeft {
+  from {
+    opacity: 0;
+    transform: translateX(-30px);
+  }
+  to {
+    opacity: 1;
+    transform: translateX(0);
+  }
+}
+
+@keyframes float {
+  0%, 100% {
+    transform: translateY(0);
+  }
+  50% {
+    transform: translateY(-10px);
+  }
+}
+
+@keyframes progressGlow {
+  0% {
+    box-shadow: 0 0 5px rgba(82, 196, 26, 0.3);
+  }
+  100% {
+    box-shadow: 0 0 20px rgba(82, 196, 26, 0.6);
+  }
+}
+
+@keyframes shimmer {
+  0% {
+    transform: translateX(-100%);
+  }
+  100% {
+    transform: translateX(100%);
+  }
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+  .history-item {
+    padding: 20px;
+  }
+  
+  .item-header {
+    flex-direction: column;
+    align-items: flex-start;
+    gap: 15px;
+  }
+  
+  .item-meta {
+    flex-direction: row;
+    align-items: center;
+    gap: 15px;
+    width: 100%;
+    justify-content: space-between;
+  }
+  
+  .item-details {
+    flex-direction: column;
+    gap: 12px;
+  }
+  
+  .detail-row {
+    justify-content: center;
+  }
+}
+
+@media (max-width: 480px) {
+  .history-item {
+    padding: 15px;
+  }
+  
+  .item-title {
+    flex-direction: column;
+    align-items: flex-start;
+    gap: 10px;
+  }
+  
+  .confidence {
+    font-size: 0.9rem;
+    padding: 4px 10px;
+  }
+  
+  .source-badge {
+    font-size: 0.8rem;
+    padding: 4px 10px;
+  }
+  
+  .detail-row {
+    font-size: 0.85rem;
+    padding: 6px 12px;
+  }
+  
+  .empty-state {
+    padding: 40px 15px;
+  }
+  
+  .empty-icon {
+    font-size: 3rem;
+  }
+  
+  .empty-state p {
+    font-size: 1rem;
+  }
+}
+</style>

src/声源定位代码/audio-classification/audio-classification-platform/frontend/src/components/PredictionResult.vue

@@ -0,0 +1,941 @@
+<template>
+  <div class="prediction-result" v-if="result">
+    <!-- 主要预测结果卡片 -->
+    <div class="main-result-card">
+      <div class="result-header">
+        <div class="result-icon">
+          <el-icon><TrophyBase /></el-icon>
+        </div>
+        <div class="result-title">
+          <h3>识别结果</h3>
+          <p>深度学习模型分析完成</p>
+        </div>
+        <div class="result-actions">
+          <el-button type="primary" size="small" @click="exportResult" class="action-btn">
+            <template #icon><el-icon><Download /></el-icon></template>
+            导出
+          </el-button>
+          <el-button size="small" @click="shareResult" class="action-btn">
+            <template #icon><el-icon><Share /></el-icon></template>
+            分享
+          </el-button>
+        </div>
+      </div>
+
+      <!-- 主要预测结果展示 -->
+      <div class="main-prediction">
+        <div class="prediction-badge">
+          <div class="badge-icon">
+            <el-icon><Star /></el-icon>
+          </div>
+          <div class="badge-content">
+            <div class="predicted-class">{{ result.predicted_class || result.label }}</div>
+            <div class="confidence-score">
+              置信度: <span class="confidence-value">{{ ((result.confidence || result.score) * 100).toFixed(2) }}%</span>
+            </div>
+          </div>
+          <div class="confidence-ring">
+            <svg class="ring-svg" viewBox="0 0 100 100">
+              <circle
+                class="ring-background"
+                cx="50"
+                cy="50"
+                r="40"
+                fill="none"
+                stroke="rgba(255, 255, 255, 0.2)"
+                stroke-width="8"
+              />
+              <circle
+                class="ring-progress"
+                cx="50"
+                cy="50"
+                r="40"
+                fill="none"
+                stroke="url(#gradient)"
+                stroke-width="8"
+                stroke-linecap="round"
+                :stroke-dasharray="circumference"
+                :stroke-dashoffset="strokeDashoffset"
+                transform="rotate(-90 50 50)"
+              />
+              <defs>
+                <linearGradient id="gradient" x1="0%" y1="0%" x2="100%" y2="0%">
+                  <stop offset="0%" style="stop-color:#52c41a"/>
+                  <stop offset="100%" style="stop-color:#73d13d"/>
+                </linearGradient>
+              </defs>
+            </svg>
+            <div class="ring-text">{{ Math.round((result.confidence || result.score) * 100) }}%</div>
+          </div>
+        </div>
+      </div>
+    </div>
+
+    <!-- 详细信息卡片 -->
+    <div class="details-card">
+      <div class="details-header">
+        <el-icon><InfoFilled /></el-icon>
+        <span>详细信息</span>
+      </div>
+      <div class="details-grid">
+        <div class="detail-item">
+          <div class="detail-icon">
+            <el-icon><Flag /></el-icon>
+          </div>
+          <div class="detail-content">
+            <div class="detail-label">预测类别</div>
+            <div class="detail-value">{{ result.predicted_class || result.label }}</div>
+          </div>
+        </div>
+        
+        <div class="detail-item">
+          <div class="detail-icon">
+            <el-icon><DataAnalysis /></el-icon>
+          </div>
+          <div class="detail-content">
+            <div class="detail-label">置信度</div>
+            <div class="detail-value">{{ ((result.confidence || result.score) * 100).toFixed(2) }}%</div>
+          </div>
+        </div>
+        
+        <div class="detail-item">
+          <div class="detail-icon">
+            <el-icon><Timer /></el-icon>
+          </div>
+          <div class="detail-content">
+            <div class="detail-label">预测时间</div>
+            <div class="detail-value">{{ formatTime(result.timestamp) }}</div>
+          </div>
+        </div>
+        
+        <div class="detail-item">
+          <div class="detail-icon">
+            <el-icon><Headphone /></el-icon>
+          </div>
+          <div class="detail-content">
+            <div class="detail-label">音频时长</div>
+            <div class="detail-value">{{ formatDuration(result.audio_info?.duration) }}</div>
+          </div>
+        </div>
+      </div>
+    </div>
+
+    <!-- 概率分布图表 -->
+    <div v-if="result.all_probabilities" class="probability-card">
+      <div class="probability-header">
+        <el-icon><PieChart /></el-icon>
+        <span>所有类别概率分布</span>
+        <el-button type="text" size="small" @click="toggleChartView" class="toggle-view">
+          <el-icon><Switch /></el-icon>
+          {{ showChart ? '列表视图' : '图表视图' }}
+        </el-button>
+      </div>
+      
+      <!-- 图表视图 -->
+      <transition name="fade">
+        <div v-if="showChart" class="chart-container">
+          <div ref="chartContainer" class="echarts-chart"></div>
+        </div>
+      </transition>
+      
+      <!-- 列表视图 -->
+      <transition name="fade">
+        <div v-if="!showChart" class="probability-list">
+          <div 
+            v-for="(prob, className) in sortedProbabilities" 
+            :key="className"
+            class="probability-item"
+            :class="{ active: className === (result.predicted_class || result.label) }"
+          >
+            <div class="prob-info">
+              <div class="class-name">
+                <span class="name-text">{{ className }}</span>
+                <el-tag v-if="className === (result.predicted_class || result.label)" 
+                       size="small" type="success" class="winner-tag">
+                  <el-icon><Trophy /></el-icon>
+                  最高
+                </el-tag>
+              </div>
+              <div class="prob-value">{{ (prob * 100).toFixed(2) }}%</div>
+            </div>            <div class="prob-bar-container">
+              <div class="prob-bar">
+                <div 
+                  class="prob-fill" 
+                  :style="{ 
+                    width: `${prob * 100}%`,
+                    background: getProgressColor(prob, className === (result.predicted_class || result.label))
+                  }"
+                ></div>
+              </div>
+            </div>
+          </div>
+        </div>
+      </transition>
+    </div>
+  </div>
+
+  <div v-else class="no-result">
+    <div class="empty-state">
+      <div class="empty-icon">
+        <el-icon><Document /></el-icon>
+      </div>
+      <h3>暂无预测结果</h3>
+      <p>请上传音频文件或录制音频进行识别</p>
+    </div>
+  </div>
+</template>
+
+<script setup>
+import { ref, computed, watch, onMounted, nextTick, onBeforeUnmount } from 'vue'
+import { ElMessage } from 'element-plus'
+import * as echarts from 'echarts'
+
+const props = defineProps({
+  result: {
+    type: Object,
+    default: null
+  }
+})
+
+// 响应式数据
+const chartContainer = ref(null)
+const showChart = ref(false)
+let chartInstance = null
+
+// 计算属性
+const circumference = computed(() => 2 * Math.PI * 40) // r=40的圆周长
+
+const strokeDashoffset = computed(() => {
+  if (!props.result) return circumference.value
+  const confidence = props.result.confidence || props.result.score || 0
+  return circumference.value - (confidence * circumference.value)
+})
+
+const sortedProbabilities = computed(() => {
+  if (!props.result?.all_probabilities) return {}
+  
+  const entries = Object.entries(props.result.all_probabilities)
+  entries.sort((a, b) => b[1] - a[1]) // 按概率降序排列
+  
+  return Object.fromEntries(entries)
+})
+
+// 格式化函数
+const formatTime = (timestamp) => {
+  if (!timestamp) return 'N/A'
+  try {
+    if (typeof timestamp === 'string') {
+      return timestamp
+    }
+    return new Date(timestamp).toLocaleString('zh-CN', {
+      year: 'numeric',
+      month: '2-digit',
+      day: '2-digit',
+      hour: '2-digit',
+      minute: '2-digit',
+      second: '2-digit'
+    })
+  } catch (error) {
+    console.error('时间格式化错误:', error)
+    return 'N/A'
+  }
+}
+
+const formatDuration = (seconds) => {
+  if (!seconds && seconds !== 0) return 'N/A'
+  
+  try {
+    const duration = parseFloat(seconds)
+    if (isNaN(duration)) return 'N/A'
+    
+    if (duration < 60) {
+      return `${duration.toFixed(1)}秒`
+    }
+    
+    const minutes = Math.floor(duration / 60)
+    const remainingSeconds = (duration % 60).toFixed(1)
+    return `${minutes}分${remainingSeconds}秒`
+  } catch (error) {
+    console.error('时长格式化错误:', error)
+    return 'N/A'
+  }
+}
+
+const getProgressColor = (prob, isWinner = false) => {
+  if (isWinner) {
+    return 'linear-gradient(135deg, #52c41a, #73d13d)'
+  }
+  if (prob > 0.7) return 'linear-gradient(135deg, #52c41a, #73d13d)'
+  if (prob > 0.4) return 'linear-gradient(135deg, #fadb14, #ffec3d)'
+  if (prob > 0.2) return 'linear-gradient(135deg, #fa8c16, #ffa940)'
+  return 'linear-gradient(135deg, #ff4d4f, #ff7875)'
+}
+
+const initChart = () => {
+  if (!props.result?.all_probabilities || !chartContainer.value) return
+  
+  if (chartInstance) {
+    chartInstance.dispose()
+  }
+  
+  chartInstance = echarts.init(chartContainer.value)
+  
+  const data = Object.entries(props.result.all_probabilities)
+    .map(([name, value]) => ({ name, value: (value * 100).toFixed(2) }))
+    .sort((a, b) => b.value - a.value)
+  
+  const option = {
+    title: {
+      text: '类别置信度分布',
+      left: 'center'
+    },
+    tooltip: {
+      trigger: 'item',
+      formatter: '{a} <br/>{b}: {c}%'
+    },
+    legend: {
+      orient: 'vertical',
+      left: 'left'
+    },
+    series: [
+      {
+        name: '置信度',
+        type: 'pie',
+        radius: '50%',
+        data: data,
+        emphasis: {
+          itemStyle: {
+            shadowBlur: 10,
+            shadowOffsetX: 0,
+            shadowColor: 'rgba(0, 0, 0, 0.5)'
+          }
+        }
+      }
+    ]
+  }
+  
+  chartInstance.setOption(option)
+}
+
+const exportResult = () => {
+  try {
+    const exportData = {
+      predicted_class: props.result.predicted_class,
+      confidence: props.result.confidence,
+      timestamp: props.result.timestamp,
+      all_probabilities: props.result.all_probabilities
+    }
+    
+    const blob = new Blob([JSON.stringify(exportData, null, 2)], { type: 'application/json' })
+    const url = URL.createObjectURL(blob)
+    const a = document.createElement('a')
+    a.href = url
+    a.download = `prediction_result_${Date.now()}.json`
+    a.click()
+    URL.revokeObjectURL(url)
+    
+    ElMessage.success('结果导出成功')
+  } catch (error) {
+    ElMessage.error('导出失败: ' + error.message)
+  }
+}
+
+const shareResult = () => {
+  const shareText = `音频分类结果: ${props.result.predicted_class} (置信度: ${(props.result.confidence * 100).toFixed(2)}%)`
+  
+  if (navigator.share) {
+    navigator.share({
+      title: '音频分类结果',
+      text: shareText
+    })
+  } else {
+    navigator.clipboard.writeText(shareText).then(() => {
+      ElMessage.success('结果已复制到剪贴板')
+    }).catch(() => {
+      ElMessage.error('复制失败')
+    })
+  }
+}
+
+watch(() => props.result, () => {
+  if (props.result) {
+    nextTick(() => {
+      initChart()
+    })
+  }
+}, { immediate: true })
+
+onMounted(() => {
+  if (props.result) {
+    nextTick(() => {
+      initChart()
+    })
+  }
+})
+</script>
+
+<style scoped>
+/* 预测结果主容器 */
+.prediction-result {
+  margin-top: 20px;
+  display: flex;
+  flex-direction: column;
+  gap: 25px;
+  animation: fadeInUp 0.6s ease-out;
+}
+
+/* 主要结果卡片 */
+.main-result-card {
+  background: rgba(255, 255, 255, 0.15);
+  backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  border-radius: 20px;
+  padding: 30px;
+  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
+  transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+  position: relative;
+  overflow: hidden;
+}
+
+.main-result-card::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: -100%;
+  width: 100%;
+  height: 100%;
+  background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent);
+  transition: left 0.5s ease;
+}
+
+.main-result-card:hover::before {
+  left: 100%;
+}
+
+.main-result-card:hover {
+  transform: translateY(-5px);
+  box-shadow: 0 20px 40px rgba(0, 0, 0, 0.15);
+}
+
+/* 结果头部 */
+.result-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 30px;
+}
+
+.result-icon {
+  width: 50px;
+  height: 50px;
+  background: linear-gradient(135deg, #52c41a, #73d13d);
+  border-radius: 50%;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  color: white;
+  font-size: 1.5rem;
+  box-shadow: 0 8px 25px rgba(82, 196, 26, 0.3);
+  animation: pulse 2s ease-in-out infinite;
+}
+
+.result-title h3 {
+  color: white;
+  font-size: 1.8rem;
+  font-weight: 700;
+  margin: 0 0 5px 0;
+  text-shadow: 0 2px 10px rgba(0, 0, 0, 0.3);
+}
+
+.result-title p {
+  color: rgba(255, 255, 255, 0.8);
+  margin: 0;
+  font-size: 1rem;
+}
+
+.result-actions {
+  display: flex;
+  gap: 15px;
+}
+
+.action-btn {
+  background: rgba(255, 255, 255, 0.1) !important;
+  border: 1px solid rgba(255, 255, 255, 0.3) !important;
+  color: white !important;
+  transition: all 0.3s ease;
+  backdrop-filter: blur(10px);
+  border-radius: 12px !important;
+}
+
+.action-btn:hover {
+  background: rgba(255, 255, 255, 0.2) !important;
+  transform: translateY(-2px);
+  box-shadow: 0 8px 25px rgba(0, 0, 0, 0.2);
+}
+
+/* 主要预测结果 */
+.main-prediction {
+  display: flex;
+  justify-content: center;
+  margin-bottom: 20px;
+}
+
+.prediction-badge {
+  display: flex;
+  align-items: center;
+  gap: 25px;
+  padding: 25px;
+  background: rgba(255, 255, 255, 0.1);
+  border-radius: 20px;
+  backdrop-filter: blur(15px);
+  border: 1px solid rgba(255, 255, 255, 0.2);
+}
+
+.badge-icon {
+  width: 60px;
+  height: 60px;
+  background: linear-gradient(135deg, #fadb14, #ffec3d);
+  border-radius: 50%;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  color: white;
+  font-size: 1.8rem;
+  box-shadow: 0 8px 25px rgba(250, 219, 20, 0.4);
+}
+
+.badge-content {
+  flex: 1;
+}
+
+.predicted-class {
+  font-size: 2rem;
+  font-weight: 700;
+  color: white;
+  margin-bottom: 8px;
+  text-shadow: 0 2px 10px rgba(0, 0, 0, 0.3);
+}
+
+.confidence-score {
+  font-size: 1.2rem;
+  color: rgba(255, 255, 255, 0.9);
+}
+
+.confidence-value {
+  font-weight: 700;
+  color: #52c41a;
+  text-shadow: 0 0 10px rgba(82, 196, 26, 0.5);
+}
+
+/* 置信度环形图 */
+.confidence-ring {
+  position: relative;
+  width: 100px;
+  height: 100px;
+}
+
+.ring-svg {
+  width: 100%;
+  height: 100%;
+  transform: rotate(-90deg);
+}
+
+.ring-background {
+  stroke: rgba(255, 255, 255, 0.2);
+}
+
+.ring-progress {
+  stroke: url(#gradient);
+  stroke-linecap: round;
+  transition: stroke-dashoffset 1s ease-in-out;
+}
+
+.ring-text {
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%);
+  font-size: 1.2rem;
+  font-weight: 700;
+  color: white;
+  text-shadow: 0 2px 10px rgba(0, 0, 0, 0.3);
+}
+
+/* 详细信息卡片 */
+.details-card {
+  background: rgba(255, 255, 255, 0.1);
+  backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  border-radius: 16px;
+  padding: 25px;
+  transition: all 0.3s ease;
+}
+
+.details-card:hover {
+  transform: translateY(-3px);
+  box-shadow: 0 15px 35px rgba(0, 0, 0, 0.1);
+}
+
+.details-header {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  margin-bottom: 20px;
+  color: white;
+  font-size: 1.3rem;
+  font-weight: 600;
+}
+
+.details-header .el-icon {
+  color: #409eff;
+  font-size: 1.4rem;
+}
+
+.details-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+  gap: 20px;
+}
+
+.detail-item {
+  display: flex;
+  align-items: center;
+  gap: 15px;
+  padding: 20px;
+  background: rgba(255, 255, 255, 0.1);
+  border-radius: 12px;
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  transition: all 0.3s ease;
+}
+
+.detail-item:hover {
+  background: rgba(255, 255, 255, 0.15);
+  transform: translateY(-2px);
+  box-shadow: 0 8px 25px rgba(0, 0, 0, 0.1);
+}
+
+.detail-icon {
+  width: 45px;
+  height: 45px;
+  background: linear-gradient(135deg, #409eff, #52c41a);
+  border-radius: 50%;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  color: white;
+  font-size: 1.2rem;
+  flex-shrink: 0;
+}
+
+.detail-content {
+  flex: 1;
+}
+
+.detail-label {
+  font-size: 0.9rem;
+  color: rgba(255, 255, 255, 0.7);
+  margin-bottom: 5px;
+}
+
+.detail-value {
+  font-size: 1.1rem;
+  font-weight: 600;
+  color: white;
+}
+
+/* 概率分布卡片 */
+.probability-card {
+  background: rgba(255, 255, 255, 0.1);
+  backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  border-radius: 16px;
+  padding: 25px;
+  transition: all 0.3s ease;
+}
+
+.probability-card:hover {
+  transform: translateY(-3px);
+  box-shadow: 0 15px 35px rgba(0, 0, 0, 0.1);
+}
+
+.probability-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  margin-bottom: 25px;
+  color: white;
+  font-size: 1.3rem;
+  font-weight: 600;
+}
+
+.probability-header .el-icon {
+  color: #fa8c16;
+  font-size: 1.4rem;
+  margin-right: 10px;
+}
+
+.toggle-view {
+  background: rgba(255, 255, 255, 0.1) !important;
+  border: 1px solid rgba(255, 255, 255, 0.3) !important;
+  color: white !important;
+  border-radius: 8px !important;
+}
+
+.toggle-view:hover {
+  background: rgba(255, 255, 255, 0.2) !important;
+}
+
+/* 图表容器 */
+.chart-container {
+  background: rgba(255, 255, 255, 0.05);
+  border-radius: 12px;
+  padding: 20px;
+  min-height: 400px;
+}
+
+.echarts-chart {
+  width: 100%;
+  height: 400px;
+}
+
+/* 概率列表 */
+.probability-list {
+  display: flex;
+  flex-direction: column;
+  gap: 15px;
+}
+
+.probability-item {
+  padding: 20px;
+  background: rgba(255, 255, 255, 0.1);
+  border-radius: 12px;
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  transition: all 0.3s ease;
+  position: relative;
+  overflow: hidden;
+}
+
+.probability-item:hover {
+  background: rgba(255, 255, 255, 0.15);
+  transform: translateX(5px);
+  box-shadow: 0 8px 25px rgba(0, 0, 0, 0.1);
+}
+
+.probability-item.active {
+  background: rgba(82, 196, 26, 0.2);
+  border-color: rgba(82, 196, 26, 0.5);
+  box-shadow: 0 0 20px rgba(82, 196, 26, 0.3);
+}
+
+.prob-info {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 12px;
+}
+
+.class-name {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+}
+
+.name-text {
+  font-size: 1.1rem;
+  font-weight: 600;
+  color: white;
+}
+
+.winner-tag {
+  background: rgba(82, 196, 26, 0.2) !important;
+  border-color: rgba(82, 196, 26, 0.5) !important;
+  color: #52c41a !important;
+}
+
+.prob-value {
+  font-size: 1.2rem;
+  font-weight: 700;
+  color: white;
+}
+
+.prob-bar-container {
+  width: 100%;
+  height: 8px;
+  background: rgba(255, 255, 255, 0.1);
+  border-radius: 4px;
+  overflow: hidden;
+}
+
+.prob-bar {
+  width: 100%;
+  height: 100%;
+  position: relative;
+}
+
+.prob-fill {
+  height: 100%;
+  border-radius: 4px;
+  transition: all 0.8s cubic-bezier(0.4, 0, 0.2, 1);
+  background: linear-gradient(90deg, #52c41a, #73d13d);
+  position: relative;
+  animation: progressFill 1s ease-out;
+}
+
+/* 空状态 */
+.no-result {
+  text-align: center;
+  padding: 60px 20px;
+  background: rgba(255, 255, 255, 0.1);
+  backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  border-radius: 16px;
+}
+
+.empty-state {
+  animation: fadeIn 0.6s ease-out;
+}
+
+.empty-icon {
+  font-size: 4rem;
+  color: rgba(255, 255, 255, 0.4);
+  margin-bottom: 20px;
+  animation: float 3s ease-in-out infinite;
+}
+
+.empty-state h3 {
+  color: white;
+  font-size: 1.5rem;
+  font-weight: 600;
+  margin-bottom: 10px;
+  text-shadow: 0 2px 10px rgba(0, 0, 0, 0.3);
+}
+
+.empty-state p {
+  color: rgba(255, 255, 255, 0.7);
+  font-size: 1.1rem;
+  margin: 0;
+}
+
+/* 动画 */
+@keyframes fadeInUp {
+  from {
+    opacity: 0;
+    transform: translateY(30px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+@keyframes fadeIn {
+  from { opacity: 0; }
+  to { opacity: 1; }
+}
+
+@keyframes pulse {
+  0%, 100% {
+    transform: scale(1);
+  }
+  50% {
+    transform: scale(1.05);
+  }
+}
+
+@keyframes float {
+  0%, 100% {
+    transform: translateY(0);
+  }
+  50% {
+    transform: translateY(-10px);
+  }
+}
+
+@keyframes progressFill {
+  from {
+    width: 0%;
+  }
+  to {
+    width: var(--target-width, 100%);
+  }
+}
+
+/* 过渡动画 */
+.fade-enter-active,
+.fade-leave-active {
+  transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.fade-enter-from,
+.fade-leave-to {
+  opacity: 0;
+  transform: translateY(20px);
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+  .prediction-result {
+    gap: 20px;
+  }
+  
+  .main-result-card {
+    padding: 20px;
+  }
+  
+  .result-header {
+    flex-direction: column;
+    gap: 15px;
+    text-align: center;
+  }
+  
+  .prediction-badge {
+    flex-direction: column;
+    text-align: center;
+    gap: 20px;
+  }
+  
+  .details-grid {
+    grid-template-columns: 1fr;
+    gap: 15px;
+  }
+  
+  .prob-info {
+    flex-direction: column;
+    gap: 10px;
+    text-align: center;
+  }
+  
+  .chart-container {
+    min-height: 300px;
+  }
+  
+  .echarts-chart {
+    height: 300px;
+  }
+}
+
+@media (max-width: 480px) {
+  .main-result-card,
+  .details-card,
+  .probability-card {
+    padding: 15px;
+  }
+  
+  .predicted-class {
+    font-size: 1.5rem;
+  }
+  
+  .confidence-ring {
+    width: 80px;
+    height: 80px;
+  }
+  
+  .detail-item {
+    padding: 15px;
+  }
+  
+  .probability-item {
+    padding: 15px;
+  }
+}
+</style>

src/声源定位代码/audio-classification/audio-classification-platform/frontend/src/main.js

@@ -0,0 +1,18 @@
+import { createApp } from 'vue'
+import App from './App.vue'
+import router from './router'
+import ElementPlus from 'element-plus'
+import 'element-plus/dist/index.css'
+import * as ElementPlusIconsVue from '@element-plus/icons-vue'
+
+const app = createApp(App)
+
+// 注册所有图标
+for (const [key, component] of Object.entries(ElementPlusIconsVue)) {
+  app.component(key, component)
+}
+
+app.use(ElementPlus)
+app.use(router)
+
+app.mount('#app')

src/声源定位代码/audio-classification/audio-classification-platform/frontend/src/router/index.js

@@ -0,0 +1,17 @@
+import { createRouter, createWebHistory } from 'vue-router'
+import HomePage from '../views/HomePage.vue'
+
+const routes = [
+  {
+    path: '/',
+    name: 'Home',
+    component: HomePage
+  }
+]
+
+const router = createRouter({
+  history: createWebHistory(),
+  routes
+})
+
+export default router

src/声源定位代码/audio-classification/audio-classification-platform/frontend/src/utils/api.js

@@ -0,0 +1,213 @@
+import axios from 'axios'
+import { ElMessage } from 'element-plus'
+
+// 创建axios实例
+const api = axios.create({
+  baseURL: '/api',
+  timeout: 30000,
+  headers: {
+    'Content-Type': 'application/json'
+  }
+})
+
+// 请求拦截器
+api.interceptors.request.use(
+  (config) => {
+    // 可以在这里添加token等认证信息
+    return config
+  },
+  (error) => {
+    return Promise.reject(error)
+  }
+)
+
+// 响应拦截器
+api.interceptors.response.use(
+  (response) => {
+    return response
+  },
+  (error) => {
+    let errorMessage = '请求失败'
+    
+    if (error.response) {
+      // 服务器返回错误状态码
+      const { status, data } = error.response
+      
+      switch (status) {
+        case 400:
+          errorMessage = data.message || '请求参数错误'
+          break
+        case 401:
+          errorMessage = '未授权访问'
+          break
+        case 403:
+          errorMessage = '禁止访问'
+          break
+        case 404:
+          errorMessage = '请求的资源不存在'
+          break
+        case 413:
+          errorMessage = '文件过大，最大支持50MB'
+          break
+        case 500:
+          errorMessage = data.message || '服务器内部错误'
+          break
+        default:
+          errorMessage = data.message || `请求失败 (${status})`
+      }
+    } else if (error.request) {
+      // 网络错误
+      errorMessage = '网络连接失败，请检查网络设置'
+    } else {
+      // 其他错误
+      errorMessage = error.message || '未知错误'
+    }
+    
+    // 显示错误消息
+    ElMessage.error(errorMessage)
+    
+    return Promise.reject(new Error(errorMessage))
+  }
+)
+
+// API服务类
+export const apiService = {
+  // 健康检查
+  healthCheck: () => {
+    return api.get('/health')
+  },
+  
+  // 初始化模型
+  initModel: (config) => {
+    return api.post('/init', config)
+  },
+    // 上传文件并预测
+  uploadAndPredict: (file, onProgress) => {
+    const formData = new FormData()
+    formData.append('file', file)
+    
+    return api.post('/upload', formData, {
+      // 不要手动设置 Content-Type，让浏览器自动设置 boundary
+      onUploadProgress: (progressEvent) => {
+        if (onProgress && progressEvent.total) {
+          const percent = Math.round((progressEvent.loaded * 100) / progressEvent.total)
+          onProgress(percent)
+        }
+      }
+    })
+  },
+  
+  // 预测音频数据（录音）
+  predictAudioData: (audioData) => {
+    return api.post('/predict', audioData)
+  },
+  
+  // 获取标签列表
+  getLabels: () => {
+    return api.get('/labels')
+  },
+  
+  // 获取模型信息
+  getModelInfo: () => {
+    return api.get('/model/info')
+  }
+}
+
+// 工具函数
+export const utils = {
+  // 格式化文件大小
+  formatFileSize: (bytes) => {
+    if (bytes === 0) return '0 B'
+    
+    const k = 1024
+    const sizes = ['B', 'KB', 'MB', 'GB', 'TB']
+    const i = Math.floor(Math.log(bytes) / Math.log(k))
+    
+    return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i]
+  },
+  
+  // 格式化时长
+  formatDuration: (seconds) => {
+    if (!seconds) return '0s'
+    
+    const hrs = Math.floor(seconds / 3600)
+    const mins = Math.floor((seconds % 3600) / 60)
+    const secs = Math.floor(seconds % 60)
+    
+    if (hrs > 0) {
+      return `${hrs}:${mins.toString().padStart(2, '0')}:${secs.toString().padStart(2, '0')}`
+    } else if (mins > 0) {
+      return `${mins}:${secs.toString().padStart(2, '0')}`
+    } else {
+      return `${secs}s`
+    }
+  },
+  
+  // 检查音频文件格式
+  isAudioFile: (file) => {
+    const allowedTypes = ['audio/wav', 'audio/mpeg', 'audio/flac', 'audio/m4a', 'audio/ogg', 'audio/aac']
+    const allowedExtensions = ['wav', 'mp3', 'flac', 'm4a', 'ogg', 'aac']
+    
+    const fileExtension = file.name.split('.').pop().toLowerCase()
+    
+    return allowedTypes.includes(file.type) || allowedExtensions.includes(fileExtension)
+  },
+  
+  // 检查文件大小
+  isFileSizeValid: (file, maxSizeMB = 50) => {
+    const maxSize = maxSizeMB * 1024 * 1024
+    return file.size <= maxSize
+  },
+  
+  // 生成唯一ID
+  generateId: () => {
+    return Date.now().toString(36) + Math.random().toString(36).substr(2)
+  },
+  
+  // 下载文件
+  downloadFile: (content, filename, type = 'application/json') => {
+    const blob = new Blob([content], { type })
+    const url = URL.createObjectURL(blob)
+    
+    const link = document.createElement('a')
+    link.href = url
+    link.download = filename
+    document.body.appendChild(link)
+    link.click()
+    document.body.removeChild(link)
+    URL.revokeObjectURL(url)
+  },
+  
+  // 复制到剪贴板
+  copyToClipboard: async (text) => {
+    try {
+      await navigator.clipboard.writeText(text)
+      return true
+    } catch (error) {
+      console.error('复制失败:', error)
+      return false
+    }
+  },
+  
+  // 获取音频时长
+  getAudioDuration: (file) => {
+    return new Promise((resolve, reject) => {
+      const audio = new Audio()
+      const url = URL.createObjectURL(file)
+      
+      audio.addEventListener('loadedmetadata', () => {
+        URL.revokeObjectURL(url)
+        resolve(audio.duration)
+      })
+      
+      audio.addEventListener('error', (error) => {
+        URL.revokeObjectURL(url)
+        reject(error)
+      })
+      
+      audio.src = url
+    })
+  }
+}
+
+export default api

src/声源定位代码/audio-classification/audio-classification-platform/frontend/src/views/HomePage.vue

@@ -0,0 +1,841 @@
+<template>
+  <div class="home-page">
+    <!-- 装饰性背景元素 -->
+    <div class="background-decoration">
+      <div class="decoration-circle circle-1"></div>
+      <div class="decoration-circle circle-2"></div>
+      <div class="decoration-circle circle-3"></div>
+    </div>
+
+    <!-- 顶部导航 -->
+    <header class="header">
+      <div class="container">
+        <div class="header-content">
+          <h1 class="title">
+            <div class="title-wrapper">
+              <el-icon class="title-icon"><Microphone /></el-icon>
+              <span class="title-text">声纹识别系统</span>
+            </div>
+            <div class="title-glow"></div>
+          </h1>
+          <div class="subtitle">
+            <span class="subtitle-text">基于深度学习的音频分类平台</span>
+            <div class="subtitle-decoration"></div>
+          </div>
+        </div>
+      </div>
+    </header>
+
+    <!-- 主要内容区域 -->
+    <main class="main-content">
+      <div class="container">
+        <!-- 系统状态卡片 -->
+        <el-card class="status-card glass-card" shadow="never">
+          <div class="status-info">
+            <div class="status-display">
+              <div class="status-icon-wrapper">
+                <el-icon class="status-icon" :class="{ active: modelStatus === 'ready' }">
+                  <Cpu />
+                </el-icon>
+              </div>
+              <div class="status-text">
+                <h3>模型状态</h3>
+                <el-tag 
+                  :type="modelStatus === 'ready' ? 'success' : 'warning'" 
+                  size="large"
+                  class="status-tag"
+                >
+                  {{ modelStatus === 'ready' ? '✓ 模型已就绪' : '⚠ 模型未初始化' }}
+                </el-tag>
+              </div>
+            </div>
+            <el-button 
+              v-if="modelStatus !== 'ready'" 
+              type="primary" 
+              @click="initModel"
+              :loading="initLoading"
+              class="init-button"
+              size="large"
+            >
+              <template #icon>
+                <el-icon><Setting /></el-icon>
+              </template>
+              初始化模型
+            </el-button>
+          </div>
+        </el-card>
+
+        <!-- 功能区域 -->
+        <div class="function-area">
+          <!-- 文件上传区域 -->
+          <el-card class="upload-card glass-card" shadow="never">
+            <template #header>
+              <div class="card-header">
+                <div class="header-icon">
+                  <el-icon><Upload /></el-icon>
+                </div>
+                <div class="header-text">
+                  <h3>音频文件上传</h3>
+                  <p>支持多种音频格式，快速识别</p>
+                </div>
+              </div>
+            </template>
+            
+            <AudioUpload 
+              @upload-success="handleUploadSuccess"
+              @upload-error="handleUploadError"
+              :disabled="modelStatus !== 'ready'"
+            />
+          </el-card>
+
+          <!-- 录音区域 -->
+          <el-card class="record-card glass-card" shadow="never">
+            <template #header>
+              <div class="card-header">
+                <div class="header-icon">
+                  <el-icon><Microphone /></el-icon>
+                </div>
+                <div class="header-text">
+                  <h3>实时录音识别</h3>
+                  <p>直接录制音频进行实时分析</p>
+                </div>
+              </div>
+            </template>
+            
+            <AudioRecorder 
+              @record-success="handleRecordSuccess"
+              @record-error="handleRecordError"
+              :disabled="modelStatus !== 'ready'"
+            />
+          </el-card>
+        </div>
+
+        <!-- 结果展示区域 -->
+        <transition name="slide-up" appear>
+          <el-card v-if="predictionResult" class="result-card glass-card" shadow="never">
+            <template #header>
+              <div class="card-header">
+                <div class="header-icon result-icon">
+                  <el-icon><DataAnalysis /></el-icon>
+                </div>
+                <div class="header-text">
+                  <h3>预测结果</h3>
+                  <p>深度学习模型分析结果</p>
+                </div>
+                <div class="result-badge">
+                  <el-icon><TrophyBase /></el-icon>
+                </div>
+              </div>
+            </template>
+            
+            <PredictionResult :result="predictionResult" />
+          </el-card>
+        </transition>
+
+        <!-- 历史记录 -->
+        <el-card class="history-card glass-card" shadow="never">
+          <template #header>
+            <div class="card-header">
+              <div class="header-icon">
+                <el-icon><Clock /></el-icon>
+              </div>
+              <div class="header-text">
+                <h3>识别历史</h3>
+                <p>查看所有识别记录和结果</p>
+              </div>
+              <el-button 
+                type="text" 
+                size="small" 
+                @click="clearHistory"
+                v-if="historyList.length > 0"
+                class="clear-button"
+              >
+                <el-icon><Delete /></el-icon>
+                清空历史
+              </el-button>
+            </div>
+          </template>
+          
+          <HistoryList :history="historyList" @select-item="handleSelectHistory" />
+        </el-card>
+      </div>
+    </main>
+
+    <!-- 底部装饰 -->
+    <div class="footer-decoration">
+      <div class="wave wave-1"></div>
+      <div class="wave wave-2"></div>
+    </div>
+  </div>
+</template>
+
+<script setup>
+import { ref, onMounted } from 'vue'
+import { ElMessage, ElNotification } from 'element-plus'
+import AudioUpload from '../components/AudioUpload.vue'
+import AudioRecorder from '../components/AudioRecorder_new.vue'
+import PredictionResult from '../components/PredictionResult.vue'
+import HistoryList from '../components/HistoryList.vue'
+import { apiService } from '../utils/api'
+
+// 响应式数据
+const modelStatus = ref('unknown') // unknown, ready, loading
+const initLoading = ref(false)
+const predictionResult = ref(null)
+const historyList = ref([])
+
+// 初始化模型
+const initModel = async () => {
+  initLoading.value = true
+  try {
+    const response = await apiService.initModel({
+      configs: '../../configs/cam++.yml',
+      model_path: '../../models/CAMPPlus_Fbank/best_model/',
+      use_gpu: true
+    })
+    
+    if (response.data.status === 'success') {
+      modelStatus.value = 'ready'
+      ElMessage.success('模型初始化成功')
+    } else {
+      throw new Error(response.data.message)
+    }
+  } catch (error) {
+    ElMessage.error(`模型初始化失败: ${error.message}`)
+    modelStatus.value = 'error'
+  } finally {
+    initLoading.value = false
+  }
+}
+
+// 检查服务器状态
+const checkServerStatus = async () => {
+  try {
+    await apiService.healthCheck()
+    // 尝试初始化模型
+    await initModel()
+  } catch (error) {
+    ElMessage.error('无法连接到服务器，请确保后端服务已启动')
+  }
+}
+
+// 处理上传成功
+const handleUploadSuccess = (result, filename) => {
+  predictionResult.value = {
+    ...result,
+    filename,
+    source: 'upload',
+    timestamp: new Date().toLocaleString()
+  }
+  
+  // 添加到历史记录
+  addToHistory(predictionResult.value)
+  
+  ElNotification({
+    title: '识别成功',
+    message: `预测结果: ${result.predicted_class || result.label} (置信度: ${((result.confidence || result.score) * 100).toFixed(2)}%)`,
+    type: 'success'
+  })
+}
+
+// 处理上传错误
+const handleUploadError = (error) => {
+  ElMessage.error(`上传失败: ${error}`)
+}
+
+// 处理录音成功
+const handleRecordSuccess = (result) => {
+  predictionResult.value = {
+    ...result,
+    source: 'record',
+    timestamp: new Date().toLocaleString()
+  }
+  
+  // 添加到历史记录
+  addToHistory(predictionResult.value)
+  
+  ElNotification({
+    title: '识别成功',
+    message: `预测结果: ${result.predicted_class || result.label} (置信度: ${((result.confidence || result.score) * 100).toFixed(2)}%)`,
+    type: 'success'
+  })
+}
+
+// 处理录音错误
+const handleRecordError = (error) => {
+  ElMessage.error(`录音识别失败: ${error}`)
+}
+
+// 添加到历史记录
+const addToHistory = (result) => {
+  historyList.value.unshift(result)
+  // 最多保留20条记录
+  if (historyList.value.length > 20) {
+    historyList.value = historyList.value.slice(0, 20)
+  }
+  // 保存到localStorage
+  localStorage.setItem('audio-classification-history', JSON.stringify(historyList.value))
+}
+
+// 选择历史记录
+const handleSelectHistory = (item) => {
+  predictionResult.value = item
+}
+
+// 清空历史记录
+const clearHistory = () => {
+  historyList.value = []
+  localStorage.removeItem('audio-classification-history')
+  ElMessage.success('历史记录已清空')
+}
+
+// 加载历史记录
+const loadHistory = () => {
+  const saved = localStorage.getItem('audio-classification-history')
+  if (saved) {
+    try {
+      historyList.value = JSON.parse(saved)
+    } catch (error) {
+      console.error('加载历史记录失败:', error)
+    }
+  }
+}
+
+// 组件挂载时执行
+onMounted(() => {
+  loadHistory()
+  checkServerStatus()
+})
+</script>
+
+<style scoped>
+.home-page {
+  min-height: 100vh;
+  position: relative;
+  overflow-x: hidden;
+}
+
+/* 装饰性背景元素 */
+.background-decoration {
+  position: fixed;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  pointer-events: none;
+  z-index: 0;
+}
+
+.decoration-circle {
+  position: absolute;
+  border-radius: 50%;
+  background: rgba(255, 255, 255, 0.1);
+  backdrop-filter: blur(10px);
+  animation: float 6s ease-in-out infinite;
+}
+
+.circle-1 {
+  width: 200px;
+  height: 200px;
+  top: 10%;
+  right: 10%;
+  animation-delay: 0s;
+}
+
+.circle-2 {
+  width: 150px;
+  height: 150px;
+  bottom: 20%;
+  left: 5%;
+  animation-delay: 2s;
+}
+
+.circle-3 {
+  width: 100px;
+  height: 100px;
+  top: 50%;
+  left: 80%;
+  animation-delay: 4s;
+}
+
+@keyframes float {
+  0%, 100% {
+    transform: translateY(0px) rotate(0deg);
+  }
+  50% {
+    transform: translateY(-20px) rotate(10deg);
+  }
+}
+
+/* 头部样式 */
+.header {
+  background: rgba(255, 255, 255, 0.1);
+  backdrop-filter: blur(20px);
+  border-bottom: 1px solid rgba(255, 255, 255, 0.2);
+  padding: 40px 0;
+  position: sticky;
+  top: 0;
+  z-index: 100;
+  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
+}
+
+.container {
+  max-width: 1400px;
+  margin: 0 auto;
+  padding: 0 20px;
+  position: relative;
+  z-index: 1;
+}
+
+.header-content {
+  text-align: center;
+  animation: fadeIn 1s ease-out;
+}
+
+.title {
+  position: relative;
+  margin-bottom: 15px;
+}
+
+.title-wrapper {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 20px;
+  font-size: 3.5rem;
+  font-weight: 800;
+  color: white;
+  text-shadow: 0 4px 20px rgba(0, 0, 0, 0.3);
+  animation: slideInLeft 1s ease-out;
+}
+
+.title-icon {
+  font-size: 3.5rem;
+  color: #fff;
+  filter: drop-shadow(0 4px 8px rgba(0, 0, 0, 0.3));
+  animation: pulse 2s ease-in-out infinite;
+}
+
+.title-text {
+  background: linear-gradient(135deg, #fff 0%, #f0f0f0 100%);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  background-clip: text;
+}
+
+.title-glow {
+  position: absolute;
+  top: 0;
+  left: 50%;
+  transform: translateX(-50%);
+  width: 100%;
+  height: 100%;
+  background: radial-gradient(ellipse at center, rgba(255, 255, 255, 0.3) 0%, transparent 70%);
+  z-index: -1;
+  animation: glow 3s ease-in-out infinite alternate;
+}
+
+@keyframes glow {
+  from {
+    opacity: 0.5;
+  }
+  to {
+    opacity: 1;
+  }
+}
+
+.subtitle {
+  position: relative;
+  animation: slideInRight 1s ease-out;
+}
+
+.subtitle-text {
+  font-size: 1.3rem;
+  color: rgba(255, 255, 255, 0.9);
+  font-weight: 300;
+  letter-spacing: 1px;
+  text-shadow: 0 2px 10px rgba(0, 0, 0, 0.2);
+}
+
+.subtitle-decoration {
+  width: 100px;
+  height: 2px;
+  background: linear-gradient(90deg, transparent 0%, rgba(255, 255, 255, 0.7) 50%, transparent 100%);
+  margin: 15px auto 0;
+  animation: fadeIn 2s ease-out;
+}
+
+/* 主内容区域 */
+.main-content {
+  padding: 50px 0;
+  position: relative;
+  z-index: 1;
+}
+
+/* 玻璃效果卡片 */
+.glass-card {
+  background: rgba(255, 255, 255, 0.15);
+  backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  border-radius: 20px;
+  box-shadow: 0 15px 35px rgba(0, 0, 0, 0.1);
+  transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
+  animation: fadeIn 0.8s ease-out;
+}
+
+.glass-card:hover {
+  transform: translateY(-10px);
+  box-shadow: 0 25px 50px rgba(0, 0, 0, 0.15);
+  background: rgba(255, 255, 255, 0.2);
+}
+
+/* 状态卡片 */
+.status-card {
+  margin-bottom: 40px;
+  animation-delay: 0.2s;
+}
+
+.status-info {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 20px;
+}
+
+.status-display {
+  display: flex;
+  align-items: center;
+  gap: 20px;
+}
+
+.status-icon-wrapper {
+  width: 60px;
+  height: 60px;
+  border-radius: 50%;
+  background: rgba(255, 255, 255, 0.2);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  transition: all 0.3s ease;
+}
+
+.status-icon {
+  font-size: 28px;
+  color: rgba(255, 255, 255, 0.8);
+  transition: all 0.3s ease;
+}
+
+.status-icon.active {
+  color: #52c41a;
+  animation: pulse 2s ease-in-out infinite;
+}
+
+.status-text h3 {
+  color: white;
+  font-size: 1.4rem;
+  font-weight: 600;
+  margin-bottom: 8px;
+}
+
+.status-tag {
+  font-size: 1rem;
+  padding: 8px 16px;
+  border-radius: 25px;
+  font-weight: 500;
+}
+
+.init-button {
+  border-radius: 25px;
+  padding: 12px 24px;
+  font-weight: 600;
+  background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+  border: none;
+  box-shadow: 0 8px 20px rgba(102, 126, 234, 0.4);
+  transition: all 0.3s ease;
+}
+
+.init-button:hover {
+  transform: translateY(-2px);
+  box-shadow: 0 12px 25px rgba(102, 126, 234, 0.6);
+}
+
+/* 功能区域 */
+.function-area {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 40px;
+  margin-bottom: 40px;
+}
+
+.upload-card {
+  animation-delay: 0.4s;
+}
+
+.record-card {
+  animation-delay: 0.6s;
+}
+
+/* 卡片头部 */
+.card-header {
+  display: flex;
+  align-items: center;
+  gap: 15px;
+  padding: 20px 0;
+  color: white;
+}
+
+.header-icon {
+  width: 50px;
+  height: 50px;
+  border-radius: 15px;
+  background: rgba(255, 255, 255, 0.2);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  transition: all 0.3s ease;
+}
+
+.header-icon .el-icon {
+  font-size: 24px;
+  color: white;
+}
+
+.result-icon {
+  background: linear-gradient(135deg, #52c41a 0%, #73d13d 100%);
+}
+
+.header-text h3 {
+  font-size: 1.3rem;
+  font-weight: 600;
+  margin-bottom: 4px;
+  color: white;
+}
+
+.header-text p {
+  font-size: 0.95rem;
+  color: rgba(255, 255, 255, 0.8);
+  margin: 0;
+}
+
+.result-badge {
+  margin-left: auto;
+  width: 40px;
+  height: 40px;
+  border-radius: 50%;
+  background: linear-gradient(135deg, #ffd700 0%, #ffed4e 100%);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  animation: pulse 2s ease-in-out infinite;
+}
+
+.result-badge .el-icon {
+  color: #fff;
+  font-size: 20px;
+}
+
+/* 结果卡片 */
+.result-card {
+  margin-bottom: 40px;
+  animation-delay: 0.8s;
+}
+
+/* 历史记录卡片 */
+.history-card {
+  animation-delay: 1s;
+}
+
+.clear-button {
+  margin-left: auto;
+  color: rgba(255, 255, 255, 0.8);
+  transition: all 0.3s ease;
+}
+
+.clear-button:hover {
+  color: #ff4d4f;
+  background: rgba(255, 77, 79, 0.1);
+}
+
+/* 动画效果 */
+.slide-up-enter-active,
+.slide-up-leave-active {
+  transition: all 0.5s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.slide-up-enter-from {
+  opacity: 0;
+  transform: translateY(30px) scale(0.95);
+}
+
+.slide-up-leave-to {
+  opacity: 0;
+  transform: translateY(-30px) scale(0.95);
+}
+
+/* 底部装饰 */
+.footer-decoration {
+  position: fixed;
+  bottom: 0;
+  left: 0;
+  width: 100%;
+  height: 200px;
+  pointer-events: none;
+  z-index: 0;
+  overflow: hidden;
+}
+
+.wave {
+  position: absolute;
+  bottom: 0;
+  left: 0;
+  width: 200%;
+  height: 100px;
+  background: rgba(255, 255, 255, 0.1);
+  animation: wave 10s linear infinite;
+}
+
+.wave-1 {
+  animation-delay: 0s;
+  opacity: 0.6;
+}
+
+.wave-2 {
+  animation-delay: -5s;
+  opacity: 0.4;
+  height: 80px;
+}
+
+@keyframes wave {
+  0% {
+    transform: translateX(-50%);
+  }
+  100% {
+    transform: translateX(-25%);
+  }
+}
+
+/* 响应式设计 */
+@media (max-width: 1200px) {
+  .container {
+    max-width: 1000px;
+  }
+  
+  .title-wrapper {
+    font-size: 3rem;
+  }
+  
+  .title-icon {
+    font-size: 3rem;
+  }
+}
+
+@media (max-width: 992px) {
+  .function-area {
+    grid-template-columns: 1fr;
+    gap: 30px;
+  }
+  
+  .title-wrapper {
+    font-size: 2.5rem;
+  }
+  
+  .title-icon {
+    font-size: 2.5rem;
+  }
+  
+  .subtitle-text {
+    font-size: 1.1rem;
+  }
+}
+
+@media (max-width: 768px) {
+  .header {
+    padding: 30px 0;
+  }
+  
+  .title-wrapper {
+    font-size: 2rem;
+    flex-direction: column;
+    gap: 10px;
+  }
+  
+  .title-icon {
+    font-size: 2rem;
+  }
+  
+  .subtitle-text {
+    font-size: 1rem;
+  }
+  
+  .status-info {
+    flex-direction: column;
+    gap: 20px;
+  }
+  
+  .card-header {
+    flex-direction: column;
+    text-align: center;
+    gap: 10px;
+  }
+  
+  .decoration-circle {
+    display: none;
+  }
+}
+
+@media (max-width: 480px) {
+  .container {
+    padding: 0 15px;
+  }
+  
+  .main-content {
+    padding: 30px 0;
+  }
+  
+  .glass-card {
+    border-radius: 15px;
+  }
+  
+  .title-wrapper {
+    font-size: 1.8rem;
+  }
+  
+  .title-icon {
+    font-size: 1.8rem;
+  }
+}
+
+/* Element Plus 组件样式重写 */
+:deep(.el-card) {
+  background: transparent;
+  border: none;
+  box-shadow: none;
+}
+
+:deep(.el-card__header) {
+  background: transparent;
+  border-bottom: 1px solid rgba(255, 255, 255, 0.1);
+  padding: 0;
+}
+
+:deep(.el-card__body) {
+  padding: 30px;
+  background: transparent;
+}
+
+:deep(.el-tag) {
+  border-radius: 20px;
+  border: none;
+  font-weight: 500;
+}
+
+:deep(.el-button) {
+  border-radius: 20px;
+  font-weight: 500;
+  transition: all 0.3s ease;
+}
+
+:deep(.el-button:hover) {
+  transform: translateY(-2px);
+}
+</style>

src/声源定位代码/audio-classification/audio-classification-platform/frontend/vite.config.js

@@ -0,0 +1,33 @@
+import { defineConfig } from 'vite'
+import vue from '@vitejs/plugin-vue'
+import AutoImport from 'unplugin-auto-import/vite'
+import Components from 'unplugin-vue-components/vite'
+import { ElementPlusResolver } from 'unplugin-vue-components/resolvers'
+
+export default defineConfig({
+  plugins: [
+    vue(),
+    AutoImport({
+      resolvers: [ElementPlusResolver()],
+    }),
+    Components({
+      resolvers: [ElementPlusResolver()],
+    }),
+  ],
+  server: {
+    port: 3000,
+    host: '0.0.0.0',
+    proxy: {
+      '/api': {
+        target: 'http://localhost:5000',
+        changeOrigin: true,
+        secure: false,
+      }
+    }
+  },
+  build: {
+    outDir: 'dist',
+    assetsDir: 'assets',
+    chunkSizeWarningLimit: 1000
+  }
+})

src/声源定位代码/audio-classification/audio-classification-platform/start.bat

@@ -0,0 +1,24 @@
+@echo off
+echo ================================
+echo    声纹识别系统启动脚本
+echo ================================
+echo.
+
+echo 正在启动后端服务...
+cd /d "%~dp0backend"
+start "后端服务" cmd /k "python app.py"
+
+echo 等待后端服务启动...
+timeout /t 3 /nobreak >nul
+
+echo 正在启动前端服务...
+cd /d "%~dp0frontend"
+start "前端服务" cmd /k "npm run dev"
+
+echo.
+echo 启动完成！
+echo 后端服务: http://localhost:5000
+echo 前端服务: http://localhost:3000
+echo.
+echo 请等待服务完全启动后访问前端地址
+pause

src/声源定位代码/audio-classification/audio-classification-platform/start.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+
+echo "================================"
+echo "    声纹识别系统启动脚本"
+echo "================================"
+echo
+
+# 获取脚本所在目录
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+echo "正在启动后端服务..."
+cd "$SCRIPT_DIR/backend"
+python app.py &
+BACKEND_PID=$!
+
+echo "等待后端服务启动..."
+sleep 3
+
+echo "正在启动前端服务..."
+cd "$SCRIPT_DIR/frontend"
+npm run dev &
+FRONTEND_PID=$!
+
+echo
+echo "启动完成！"
+echo "后端服务: http://localhost:5000"
+echo "前端服务: http://localhost:3000"
+echo
+echo "后端进程 PID: $BACKEND_PID"
+echo "前端进程 PID: $FRONTEND_PID"
+echo
+echo "按 Ctrl+C 停止所有服务"
+
+# 等待用户中断
+trap 'echo "正在停止服务..."; kill $BACKEND_PID $FRONTEND_PID; exit' INT
+wait

src/声源定位代码/audio-classification/configs/augmentation.yml

@@ -0,0 +1,46 @@
+# 语速增强
+speed:
+  # 增强概率
+  prob: 1.0
+
+# 音量增强
+volume:
+  # 增强概率
+  prob: 0.0
+  # 最小增益
+  min_gain_dBFS: -15
+  # 最大增益
+  max_gain_dBFS: 15
+
+# 噪声增强
+noise:
+  # 增强概率
+  prob: 0.5
+  # 噪声增强的噪声文件夹
+  noise_dir: 'dataset/noise'
+  # 针对噪声的最小音量增益
+  min_snr_dB: 10
+  # 针对噪声的最大音量增益
+  max_snr_dB: 50
+
+# 混响增强
+reverb:
+  # 增强概率
+  prob: 0.5
+  # 混响增强的混响文件夹
+  reverb_dir: 'dataset/reverb'
+
+# Spec增强
+spec_aug:
+  # 增强概率
+  prob: 0.5
+  # 频域掩蔽的比例
+  freq_mask_ratio: 0.1
+  # 频域掩蔽次数
+  n_freq_masks: 1
+  # 频域掩蔽的比例
+  time_mask_ratio: 0.05
+  # 频域掩蔽次数
+  n_time_masks: 1
+  # 最大时间扭曲
+  max_time_warp: 0

src/声源定位代码/audio-classification/configs/cam++.yml

@@ -0,0 +1,80 @@
+# 数据集参数
+dataset_conf:
+  dataset:
+    # 过滤最短的音频长度
+    min_duration: 0.4
+    # 最长的音频长度，大于这个长度会裁剪掉
+    max_duration: 3
+    # 音频的采样率
+    sample_rate: 16000
+    # 是否对音频进行音量归一化
+    use_dB_normalization: True
+    # 对音频进行音量归一化的音量分贝值
+    target_dB: -20
+  dataLoader:
+    # 训练的批量大小
+    batch_size: 64
+    # 是否丢弃最后一个样本
+    drop_last: True
+    # 读取数据的线程数量
+    num_workers: 8
+  # 评估的数据要特殊处理
+  eval_conf:
+    # 评估的批量大小
+    batch_size: 8
+    # 最长的音频长度
+    max_duration: 20
+  # 训练数据的数据列表路径
+  train_list: 'dataset/train_list.txt'
+  # 测试数据的数据列表路径
+  test_list: 'dataset/test_list.txt'
+  # 标签列表
+  label_list_path: 'dataset/label_list.txt'
+
+# 数据预处理参数
+preprocess_conf:
+  # 是否使用HF上的Wav2Vec2类似模型提取音频特征
+  use_hf_model: False
+  # 音频预处理方法，也可以叫特征提取方法
+  # 当use_hf_model为False时，支持：MelSpectrogram、Spectrogram、MFCC、Fbank
+  # 当use_hf_model为True时，指定的是HuggingFace的模型或者本地路径，比如facebook/w2v-bert-2.0或者./feature_models/w2v-bert-2.0
+  feature_method: 'Fbank'
+  # 当use_hf_model为False时，设置API参数，更参数查看对应API，不清楚的可以直接删除该部分，直接使用默认值。
+  # 当use_hf_model为True时，可以设置参数use_gpu，指定是否使用GPU提取特征
+  method_args:
+    sample_frequency: 16000
+    num_mel_bins: 80
+
+model_conf:
+  # 所使用的模型
+  model: 'CAMPPlus'
+  # 模型参数
+  model_args:
+    # 分类大小，如果为null，自动通过标签列表获取
+    num_class: null
+
+optimizer_conf:
+  # 优化方法
+  optimizer: 'Adam'
+  # 优化方法参数
+  optimizer_args:
+    lr: 0.001
+    weight_decay: !!float 1e-5
+  # 学习率衰减函数，支持Pytorch支持的和项目提供的WarmupCosineSchedulerLR
+  scheduler: 'WarmupCosineSchedulerLR'
+  # 学习率衰减函数参数
+  scheduler_args:
+    min_lr: !!float 1e-5
+    max_lr: 0.001
+    warmup_epoch: 5
+
+train_conf:
+  # 是否开启自动混合精度
+  enable_amp: False
+  # 是否使用Pytorch2.0的编译器
+  use_compile: False
+  # CrossEntropyLoss类的label_smoothing参数
+  label_smoothing: 0.0
+  # 训练的轮数
+  max_epoch: 60
+  log_interval: 10

src/声源定位代码/audio-classification/configs/ecapa_tdnn.yml

@@ -0,0 +1,80 @@
+# 数据集参数
+dataset_conf:
+  dataset:
+    # 过滤最短的音频长度
+    min_duration: 0.4
+    # 最长的音频长度，大于这个长度会裁剪掉
+    max_duration: 3
+    # 音频的采样率
+    sample_rate: 16000
+    # 是否对音频进行音量归一化
+    use_dB_normalization: True
+    # 对音频进行音量归一化的音量分贝值
+    target_dB: -20
+  dataLoader:
+    # 训练的批量大小
+    batch_size: 128
+    # 是否丢弃最后一个样本
+    drop_last: True
+    # 读取数据的线程数量
+    num_workers: 8
+  # 评估的数据要特殊处理
+  eval_conf:
+    # 评估的批量大小
+    batch_size: 16
+    # 最长的音频长度
+    max_duration: 20
+  # 训练数据的数据列表路径
+  train_list: 'dataset/train_list.txt'
+  # 测试数据的数据列表路径
+  test_list: 'dataset/test_list.txt'
+  # 标签列表
+  label_list_path: 'dataset/label_list.txt'
+
+# 数据预处理参数
+preprocess_conf:
+  # 是否使用HF上的Wav2Vec2类似模型提取音频特征
+  use_hf_model: False
+  # 音频预处理方法，也可以叫特征提取方法
+  # 当use_hf_model为False时，支持：MelSpectrogram、Spectrogram、MFCC、Fbank
+  # 当use_hf_model为True时，指定的是HuggingFace的模型或者本地路径，比如facebook/w2v-bert-2.0或者./feature_models/w2v-bert-2.0
+  feature_method: 'Fbank'
+  # 当use_hf_model为False时，设置API参数，更参数查看对应API，不清楚的可以直接删除该部分，直接使用默认值。
+  # 当use_hf_model为True时，可以设置参数use_gpu，指定是否使用GPU提取特征
+  method_args:
+    sample_frequency: 16000
+    num_mel_bins: 80
+
+model_conf:
+  # 所使用的模型
+  model: 'EcapaTdnn'
+  # 模型参数
+  model_args:
+    # 分类大小，如果为null，自动通过标签列表获取
+    num_class: null
+
+optimizer_conf:
+  # 优化方法
+  optimizer: 'Adam'
+  # 优化方法参数
+  optimizer_args:
+    lr: 0.001
+    weight_decay: !!float 1e-5
+  # 学习率衰减函数，支持Pytorch支持的和项目提供的WarmupCosineSchedulerLR
+  scheduler: 'WarmupCosineSchedulerLR'
+  # 学习率衰减函数参数
+  scheduler_args:
+    min_lr: !!float 1e-5
+    max_lr: 0.001
+    warmup_epoch: 5
+
+train_conf:
+  # 是否开启自动混合精度
+  enable_amp: False
+  # 是否使用Pytorch2.0的编译器
+  use_compile: False
+  # CrossEntropyLoss类的label_smoothing参数
+  label_smoothing: 0.0
+  # 训练的轮数
+  max_epoch: 60
+  log_interval: 10

src/声源定位代码/audio-classification/configs/eres2net.yml

@@ -0,0 +1,80 @@
+# 数据集参数
+dataset_conf:
+  dataset:
+    # 过滤最短的音频长度
+    min_duration: 0.4
+    # 最长的音频长度，大于这个长度会裁剪掉
+    max_duration: 3
+    # 音频的采样率
+    sample_rate: 16000
+    # 是否对音频进行音量归一化
+    use_dB_normalization: True
+    # 对音频进行音量归一化的音量分贝值
+    target_dB: -20
+  dataLoader:
+    # 训练的批量大小
+    batch_size: 32
+    # 是否丢弃最后一个样本
+    drop_last: True
+    # 读取数据的线程数量
+    num_workers: 8
+  # 评估的数据要特殊处理
+  eval_conf:
+    # 评估的批量大小
+    batch_size: 4
+    # 最长的音频长度
+    max_duration: 20
+  # 训练数据的数据列表路径
+  train_list: 'dataset/train_list.txt'
+  # 测试数据的数据列表路径
+  test_list: 'dataset/test_list.txt'
+  # 标签列表
+  label_list_path: 'dataset/label_list.txt'
+
+# 数据预处理参数
+preprocess_conf:
+  # 是否使用HF上的Wav2Vec2类似模型提取音频特征
+  use_hf_model: False
+  # 音频预处理方法，也可以叫特征提取方法
+  # 当use_hf_model为False时，支持：MelSpectrogram、Spectrogram、MFCC、Fbank
+  # 当use_hf_model为True时，指定的是HuggingFace的模型或者本地路径，比如facebook/w2v-bert-2.0或者./feature_models/w2v-bert-2.0
+  feature_method: 'Fbank'
+  # 当use_hf_model为False时，设置API参数，更参数查看对应API，不清楚的可以直接删除该部分，直接使用默认值。
+  # 当use_hf_model为True时，可以设置参数use_gpu，指定是否使用GPU提取特征
+  method_args:
+    sample_frequency: 16000
+    num_mel_bins: 80
+
+model_conf:
+  # 所使用的模型，支持ERes2Net、ERes2NetV2
+  model: 'ERes2Net'
+  # 模型参数
+  model_args:
+    # 分类大小，如果为null，自动通过标签列表获取
+    num_class: null
+
+optimizer_conf:
+  # 优化方法
+  optimizer: 'Adam'
+  # 优化方法参数
+  optimizer_args:
+    lr: 0.001
+    weight_decay: !!float 1e-5
+  # 学习率衰减函数，支持Pytorch支持的和项目提供的WarmupCosineSchedulerLR
+  scheduler: 'WarmupCosineSchedulerLR'
+  # 学习率衰减函数参数
+  scheduler_args:
+    min_lr: !!float 1e-5
+    max_lr: 0.001
+    warmup_epoch: 5
+
+train_conf:
+  # 是否开启自动混合精度
+  enable_amp: False
+  # 是否使用Pytorch2.0的编译器
+  use_compile: False
+  # CrossEntropyLoss类的label_smoothing参数
+  label_smoothing: 0.0
+  # 训练的轮数
+  max_epoch: 60
+  log_interval: 10

src/声源定位代码/audio-classification/configs/panns.yml

@@ -0,0 +1,80 @@
+# 数据集参数
+dataset_conf:
+  dataset:
+    # 过滤最短的音频长度
+    min_duration: 0.4
+    # 最长的音频长度，大于这个长度会裁剪掉
+    max_duration: 3
+    # 音频的采样率
+    sample_rate: 16000
+    # 是否对音频进行音量归一化
+    use_dB_normalization: True
+    # 对音频进行音量归一化的音量分贝值
+    target_dB: -20
+  dataLoader:
+    # 训练的批量大小
+    batch_size: 64
+    # 是否丢弃最后一个样本
+    drop_last: True
+    # 读取数据的线程数量
+    num_workers: 8
+  # 评估的数据要特殊处理
+  eval_conf:
+    # 评估的批量大小
+    batch_size: 8
+    # 最长的音频长度
+    max_duration: 20
+  # 训练数据的数据列表路径
+  train_list: 'dataset/train_list.txt'
+  # 测试数据的数据列表路径
+  test_list: 'dataset/test_list.txt'
+  # 标签列表
+  label_list_path: 'dataset/label_list.txt'
+
+# 数据预处理参数
+preprocess_conf:
+  # 是否使用HF上的Wav2Vec2类似模型提取音频特征
+  use_hf_model: False
+  # 音频预处理方法，也可以叫特征提取方法
+  # 当use_hf_model为False时，支持：MelSpectrogram、Spectrogram、MFCC、Fbank
+  # 当use_hf_model为True时，指定的是HuggingFace的模型或者本地路径，比如facebook/w2v-bert-2.0或者./feature_models/w2v-bert-2.0
+  feature_method: 'Fbank'
+  # 当use_hf_model为False时，设置API参数，更参数查看对应API，不清楚的可以直接删除该部分，直接使用默认值。
+  # 当use_hf_model为True时，可以设置参数use_gpu，指定是否使用GPU提取特征
+  method_args:
+    sample_frequency: 16000
+    num_mel_bins: 80
+
+model_conf:
+# 所使用的模型，支持PANNS_CNN6、PANNS_CNN10、PANNS_CNN14
+  model: 'PANNS_CNN10'
+  # 模型参数
+  model_args:
+    # 分类大小，如果为null，自动通过标签列表获取
+    num_class: null
+
+optimizer_conf:
+  # 优化方法
+  optimizer: 'Adam'
+  # 优化方法参数
+  optimizer_args:
+    lr: 0.001
+    weight_decay: !!float 1e-5
+  # 学习率衰减函数，支持Pytorch支持的和项目提供的WarmupCosineSchedulerLR
+  scheduler: 'WarmupCosineSchedulerLR'
+  # 学习率衰减函数参数
+  scheduler_args:
+    min_lr: !!float 1e-5
+    max_lr: 0.001
+    warmup_epoch: 5
+
+train_conf:
+  # 是否开启自动混合精度
+  enable_amp: False
+  # 是否使用Pytorch2.0的编译器
+  use_compile: False
+  # CrossEntropyLoss类的label_smoothing参数
+  label_smoothing: 0.0
+  # 训练的轮数
+  max_epoch: 60
+  log_interval: 10

src/声源定位代码/audio-classification/configs/res2net.yml

@@ -0,0 +1,80 @@
+# 数据集参数
+dataset_conf:
+  dataset:
+    # 过滤最短的音频长度
+    min_duration: 0.4
+    # 最长的音频长度，大于这个长度会裁剪掉
+    max_duration: 3
+    # 音频的采样率
+    sample_rate: 16000
+    # 是否对音频进行音量归一化
+    use_dB_normalization: True
+    # 对音频进行音量归一化的音量分贝值
+    target_dB: -20
+  dataLoader:
+    # 训练的批量大小
+    batch_size: 32
+    # 是否丢弃最后一个样本
+    drop_last: True
+    # 读取数据的线程数量
+    num_workers: 8
+  # 评估的数据要特殊处理
+  eval_conf:
+    # 评估的批量大小
+    batch_size: 4
+    # 最长的音频长度
+    max_duration: 20
+  # 训练数据的数据列表路径
+  train_list: 'dataset/train_list.txt'
+  # 测试数据的数据列表路径
+  test_list: 'dataset/test_list.txt'
+  # 标签列表
+  label_list_path: 'dataset/label_list.txt'
+
+# 数据预处理参数
+preprocess_conf:
+  # 是否使用HF上的Wav2Vec2类似模型提取音频特征
+  use_hf_model: False
+  # 音频预处理方法，也可以叫特征提取方法
+  # 当use_hf_model为False时，支持：MelSpectrogram、Spectrogram、MFCC、Fbank
+  # 当use_hf_model为True时，指定的是HuggingFace的模型或者本地路径，比如facebook/w2v-bert-2.0或者./feature_models/w2v-bert-2.0
+  feature_method: 'Fbank'
+  # 当use_hf_model为False时，设置API参数，更参数查看对应API，不清楚的可以直接删除该部分，直接使用默认值。
+  # 当use_hf_model为True时，可以设置参数use_gpu，指定是否使用GPU提取特征
+  method_args:
+    sample_frequency: 16000
+    num_mel_bins: 80
+
+model_conf:
+  # 所使用的模型
+  model: 'Res2Net'
+  # 模型参数
+  model_args:
+    # 分类大小，如果为null，自动通过标签列表获取
+    num_class: null
+
+optimizer_conf:
+  # 优化方法
+  optimizer: 'Adam'
+  # 优化方法参数
+  optimizer_args:
+    lr: 0.001
+    weight_decay: !!float 1e-5
+  # 学习率衰减函数，支持Pytorch支持的和项目提供的WarmupCosineSchedulerLR
+  scheduler: 'WarmupCosineSchedulerLR'
+  # 学习率衰减函数参数
+  scheduler_args:
+    min_lr: !!float 1e-5
+    max_lr: 0.001
+    warmup_epoch: 5
+
+train_conf:
+  # 是否开启自动混合精度
+  enable_amp: False
+  # 是否使用Pytorch2.0的编译器
+  use_compile: False
+  # CrossEntropyLoss类的label_smoothing参数
+  label_smoothing: 0.0
+  # 训练的轮数
+  max_epoch: 60
+  log_interval: 10

src/声源定位代码/audio-classification/configs/resnet_se.yml

@@ -0,0 +1,80 @@
+# 数据集参数
+dataset_conf:
+  dataset:
+    # 过滤最短的音频长度
+    min_duration: 0.4
+    # 最长的音频长度，大于这个长度会裁剪掉
+    max_duration: 3
+    # 音频的采样率
+    sample_rate: 16000
+    # 是否对音频进行音量归一化
+    use_dB_normalization: True
+    # 对音频进行音量归一化的音量分贝值
+    target_dB: -20
+  dataLoader:
+    # 训练的批量大小
+    batch_size: 32
+    # 是否丢弃最后一个样本
+    drop_last: True
+    # 读取数据的线程数量
+    num_workers: 8
+  # 评估的数据要特殊处理
+  eval_conf:
+    # 评估的批量大小
+    batch_size: 4
+    # 最长的音频长度
+    max_duration: 20
+  # 训练数据的数据列表路径
+  train_list: 'dataset/train_list.txt'
+  # 测试数据的数据列表路径
+  test_list: 'dataset/test_list.txt'
+  # 标签列表
+  label_list_path: 'dataset/label_list.txt'
+
+# 数据预处理参数
+preprocess_conf:
+  # 是否使用HF上的Wav2Vec2类似模型提取音频特征
+  use_hf_model: False
+  # 音频预处理方法，也可以叫特征提取方法
+  # 当use_hf_model为False时，支持：MelSpectrogram、Spectrogram、MFCC、Fbank
+  # 当use_hf_model为True时，指定的是HuggingFace的模型或者本地路径，比如facebook/w2v-bert-2.0或者./feature_models/w2v-bert-2.0
+  feature_method: 'Fbank'
+  # 当use_hf_model为False时，设置API参数，更参数查看对应API，不清楚的可以直接删除该部分，直接使用默认值。
+  # 当use_hf_model为True时，可以设置参数use_gpu，指定是否使用GPU提取特征
+  method_args:
+    sample_frequency: 16000
+    num_mel_bins: 80
+
+model_conf:
+  # 所使用的模型
+  model: 'ResNetSE'
+  # 模型参数
+  model_args:
+    # 分类大小，如果为null，自动通过标签列表获取
+    num_class: null
+
+optimizer_conf:
+  # 优化方法
+  optimizer: 'Adam'
+  # 优化方法参数
+  optimizer_args:
+    lr: 0.001
+    weight_decay: !!float 1e-5
+  # 学习率衰减函数，支持Pytorch支持的和项目提供的WarmupCosineSchedulerLR
+  scheduler: 'WarmupCosineSchedulerLR'
+  # 学习率衰减函数参数
+  scheduler_args:
+    min_lr: !!float 1e-5
+    max_lr: 0.001
+    warmup_epoch: 5
+
+train_conf:
+  # 是否开启自动混合精度
+  enable_amp: False
+  # 是否使用Pytorch2.0的编译器
+  use_compile: False
+  # CrossEntropyLoss类的label_smoothing参数
+  label_smoothing: 0.0
+  # 训练的轮数
+  max_epoch: 60
+  log_interval: 10

src/声源定位代码/audio-classification/configs/tdnn.yml

@@ -0,0 +1,80 @@
+# 数据集参数
+dataset_conf:
+  dataset:
+    # 过滤最短的音频长度
+    min_duration: 0.4
+    # 最长的音频长度，大于这个长度会裁剪掉
+    max_duration: 3
+    # 音频的采样率
+    sample_rate: 16000
+    # 是否对音频进行音量归一化
+    use_dB_normalization: True
+    # 对音频进行音量归一化的音量分贝值
+    target_dB: -20
+  dataLoader:
+    # 训练的批量大小
+    batch_size: 64
+    # 是否丢弃最后一个样本
+    drop_last: True
+    # 读取数据的线程数量
+    num_workers: 8
+  # 评估的数据要特殊处理
+  eval_conf:
+    # 评估的批量大小
+    batch_size: 8
+    # 最长的音频长度
+    max_duration: 20
+  # 训练数据的数据列表路径
+  train_list: 'dataset/train_list.txt'
+  # 测试数据的数据列表路径
+  test_list: 'dataset/test_list.txt'
+  # 标签列表
+  label_list_path: 'dataset/label_list.txt'
+
+# 数据预处理参数
+preprocess_conf:
+  # 是否使用HF上的Wav2Vec2类似模型提取音频特征
+  use_hf_model: False
+  # 音频预处理方法，也可以叫特征提取方法
+  # 当use_hf_model为False时，支持：MelSpectrogram、Spectrogram、MFCC、Fbank
+  # 当use_hf_model为True时，指定的是HuggingFace的模型或者本地路径，比如facebook/w2v-bert-2.0或者./feature_models/w2v-bert-2.0
+  feature_method: 'Fbank'
+  # 当use_hf_model为False时，设置API参数，更参数查看对应API，不清楚的可以直接删除该部分，直接使用默认值。
+  # 当use_hf_model为True时，可以设置参数use_gpu，指定是否使用GPU提取特征
+  method_args:
+    sample_frequency: 16000
+    num_mel_bins: 80
+
+model_conf:
+  # 所使用的模型
+  model: 'TDNN'
+  # 模型参数
+  model_args:
+    # 分类大小，如果为null，自动通过标签列表获取
+    num_class: null
+
+optimizer_conf:
+  # 优化方法
+  optimizer: 'Adam'
+  # 优化方法参数
+  optimizer_args:
+    lr: 0.001
+    weight_decay: !!float 1e-5
+  # 学习率衰减函数，支持Pytorch支持的和项目提供的WarmupCosineSchedulerLR
+  scheduler: 'WarmupCosineSchedulerLR'
+  # 学习率衰减函数参数
+  scheduler_args:
+    min_lr: !!float 1e-5
+    max_lr: 0.001
+    warmup_epoch: 5
+
+train_conf:
+  # 是否开启自动混合精度
+  enable_amp: False
+  # 是否使用Pytorch2.0的编译器
+  use_compile: False
+  # CrossEntropyLoss类的label_smoothing参数
+  label_smoothing: 0.0
+  # 训练的轮数
+  max_epoch: 60
+  log_interval: 10

src/声源定位代码/audio-classification/create_data.py

@@ -0,0 +1,99 @@
+import os
+
+
+# 生成数据列表
+def get_data_list(audio_path, list_path):
+    sound_sum = 0
+    audios = os.listdir(audio_path)
+    os.makedirs(list_path, exist_ok=True)
+    f_train = open(os.path.join(list_path, 'train_list.txt'), 'w', encoding='utf-8')
+    f_test = open(os.path.join(list_path, 'test_list.txt'), 'w', encoding='utf-8')
+    f_label = open(os.path.join(list_path, 'label_list.txt'), 'w', encoding='utf-8')
+
+    for i in range(len(audios)):
+        f_label.write(f'{audios[i]}\n')
+        sounds = os.listdir(os.path.join(audio_path, audios[i]))
+        for sound in sounds:
+            sound_path = os.path.join(audio_path, audios[i], sound).replace('\\', '/')
+            if sound_sum % 10 == 0:
+                f_test.write(f'{sound_path}\t{i}\n')
+            else:
+                f_train.write(f'{sound_path}\t{i}\n')
+            sound_sum += 1
+        print(f"Audio：{i + 1}/{len(audios)}")
+    f_label.close()
+    f_test.close()
+    f_train.close()
+
+
+# 下载数据方式，执行：./tools/download_3dspeaker_data.sh
+# 生成生成方言数据列表
+def get_language_identification_data_list(audio_path, list_path):
+    labels_dict = {0: 'Standard Mandarin', 3: 'Southwestern Mandarin', 6: 'Central Plains Mandarin',
+                   4: 'JiangHuai Mandarin', 2: 'Wu dialect', 8: 'Gan dialect', 9: 'Jin dialect',
+                   11: 'LiaoJiao Mandarin', 12: 'JiLu Mandarin', 10: 'Min dialect', 7: 'Yue dialect',
+                   5: 'Hakka dialect', 1: 'Xiang dialect', 13: 'Northern Mandarin'}
+
+    with open(os.path.join(list_path, 'train_list.txt'), 'w', encoding='utf-8') as f:
+        train_dir = os.path.join(audio_path, 'train')
+        for root,  dirs, files in os.walk(train_dir):
+            for file in files:
+                if not file.endswith('.wav'): continue
+                label = int(file.split('_')[-1].replace('.wav', '')[-2:])
+                file = os.path.join(root, file)
+                f.write(f'{file}\t{label}\n')
+
+    with open(os.path.join(list_path, 'test_list.txt'), 'w', encoding='utf-8') as f:
+        test_dir = os.path.join(audio_path, 'test')
+        for root,  dirs, files in os.walk(test_dir):
+            for file in files:
+                if not file.endswith('.wav'): continue
+                label = int(file.split('_')[-1].replace('.wav', '')[-2:])
+                file = os.path.join(root, file)
+                f.write(f'{file}\t{label}\n')
+
+    with open(os.path.join(list_path, 'label_list.txt'), 'w', encoding='utf-8') as f:
+        for i in range(len(labels_dict)):
+            f.write(f'{labels_dict[i]}\n')
+
+
+# 创建UrbanSound8K数据列表
+def create_UrbanSound8K_list(audio_path, metadata_path, list_path):
+    sound_sum = 0
+
+    f_train = open(os.path.join(list_path, 'train_list.txt'), 'w', encoding='utf-8')
+    f_test = open(os.path.join(list_path, 'test_list.txt'), 'w', encoding='utf-8')
+    f_label = open(os.path.join(list_path, 'label_list.txt'), 'w', encoding='utf-8')
+
+    with open(metadata_path) as f:
+        lines = f.readlines()
+
+    labels = {}
+    for i, line in enumerate(lines):
+        if i == 0:continue
+        data = line.replace('\n', '').split(',')
+        class_id = int(data[6])
+        if class_id not in labels.keys():
+            labels[class_id] = data[-1]
+        sound_path = os.path.join(audio_path, f'fold{data[5]}', data[0]).replace('\\', '/')
+        if sound_sum % 10 == 0:
+            f_test.write(f'{sound_path}\t{data[6]}\n')
+        else:
+            f_train.write(f'{sound_path}\t{data[6]}\n')
+        sound_sum += 1
+    for i in range(len(labels)):
+        f_label.write(f'{labels[i]}\n')
+    f_label.close()
+    f_test.close()
+    f_train.close()
+
+
+if __name__ == '__main__':
+    # get_data_list('dataset/audio', 'dataset')
+    # 生成生成方言数据列表
+    # get_language_identification_data_list(audio_path='dataset/language',
+    #                                       list_path='dataset/')
+    # 创建UrbanSound8K数据列表
+    create_UrbanSound8K_list(audio_path='dataset/UrbanSound8K/audio',
+                             metadata_path='dataset/UrbanSound8K/metadata/UrbanSound8K.csv',
+                             list_path='dataset')

src/声源定位代码/audio-classification/eval.py

@@ -0,0 +1,26 @@
+import argparse
+import functools
+import time
+
+from macls.trainer import MAClsTrainer
+from macls.utils.utils import add_arguments, print_arguments
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+add_arg('configs',          str,   'configs/cam++.yml',         "配置文件")
+add_arg("use_gpu",          bool,  True,                        "是否使用GPU评估模型")
+add_arg('save_matrix_path', str,   'output/images/',            "保存混合矩阵的路径")
+add_arg('resume_model',     str,   'models/CAMPPlus_Fbank/best_model/',  "模型的路径")
+add_arg('overwrites',       str,    None,    '覆盖配置文件中的参数，比如"train_conf.max_epoch=100"，多个用逗号隔开')
+args = parser.parse_args()
+print_arguments(args=args)
+
+# 获取训练器
+trainer = MAClsTrainer(configs=args.configs, use_gpu=args.use_gpu, overwrites=args.overwrites)
+
+# 开始评估
+start = time.time()
+loss, accuracy = trainer.evaluate(resume_model=args.resume_model,
+                                  save_matrix_path=args.save_matrix_path)
+end = time.time()
+print('评估消耗时间：{}s，loss：{:.5f}，accuracy：{:.5f}'.format(int(end - start), loss, accuracy))

src/声源定位代码/audio-classification/extract_features.py

@@ -0,0 +1,19 @@
+import argparse
+import functools
+
+from macls.trainer import MAClsTrainer
+from macls.utils.utils import add_arguments, print_arguments
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+add_arg('configs',          str,    'configs/cam++.yml',        '配置文件')
+add_arg('save_dir',         str,    'dataset/features',         '保存特征的路径')
+add_arg('max_duration',    int,     100,                        '提取特征的最大时长，避免过长显存不足，单位秒')
+args = parser.parse_args()
+print_arguments(args=args)
+
+# 获取训练器
+trainer = MAClsTrainer(configs=args.configs)
+
+# 提取特征保存文件
+trainer.extract_features(save_dir=args.save_dir, max_duration=args.max_duration)

src/声源定位代码/audio-classification/infer.py

@@ -0,0 +1,23 @@
+import argparse
+import functools
+
+from macls.predict import MAClsPredictor
+from macls.utils.utils import add_arguments, print_arguments
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+add_arg('configs',          str,    'configs/cam++.yml',   '配置文件')
+add_arg('use_gpu',          bool,   True,                  '是否使用GPU预测')
+add_arg('audio_path',       str,    'dataset/UrbanSound8K/audio/fold5/156634-5-2-5.wav', '音频路径')
+add_arg('model_path',       str,    'models/CAMPPlus_Fbank/best_model/', '导出的预测模型文件路径')
+args = parser.parse_args()
+print_arguments(args=args)
+
+# 获取识别器
+predictor = MAClsPredictor(configs=args.configs,
+                           model_path=args.model_path,
+                           use_gpu=args.use_gpu)
+
+label, score = predictor.predict(audio_data=args.audio_path)
+
+print(f'音频：{args.audio_path} 的预测结果标签为：{label}，得分：{score}')

src/声源定位代码/audio-classification/infer_record.py

@@ -0,0 +1,58 @@
+import argparse
+import functools
+import threading
+import time
+
+import numpy as np
+import soundcard as sc
+
+from macls.predict import MAClsPredictor
+from macls.utils.utils import add_arguments, print_arguments
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+add_arg('configs',          str,    'configs/cam++.yml',   '配置文件')
+add_arg('use_gpu',          bool,   True,                  '是否使用GPU预测')
+add_arg('record_seconds',   float,  3,                     '录音长度')
+add_arg('model_path',       str,    'models/CAMPPlus_Fbank/best_model/', '导出的预测模型文件路径')
+args = parser.parse_args()
+print_arguments(args=args)
+
+# 获取识别器
+predictor = MAClsPredictor(configs=args.configs,
+                           model_path=args.model_path,
+                           use_gpu=args.use_gpu)
+
+all_data = []
+# 获取默认麦克风
+default_mic = sc.default_microphone()
+# 录音采样率
+samplerate = 16000
+# 录音块大小
+numframes = 1024
+# 模型输入长度
+infer_len = int(samplerate * args.record_seconds / numframes)
+
+
+def infer_thread():
+    global all_data
+    s = time.time()
+    while True:
+        if len(all_data) < infer_len: continue
+        # 截取最新的音频数据
+        seg_data = all_data[-infer_len:]
+        d = np.concatenate(seg_data)
+        # 删除旧的音频数据
+        del all_data[:len(all_data) - infer_len]
+        label, score = predictor.predict(audio_data=d, sample_rate=samplerate)
+        print(f'{int(time.time() - s)}s 预测结果标签为：{label}，得分：{score}')
+
+
+thread = threading.Thread(target=infer_thread, args=())
+thread.start()
+
+
+with default_mic.recorder(samplerate=samplerate, channels=1) as mic:
+    while True:
+        data = mic.record(numframes=numframes)
+        all_data.append(data)

src/声源定位代码/audio-classification/macls/__init__.py

@@ -0,0 +1 @@
+__version__ = "1.0.6"

src/声源定位代码/audio-classification/macls/data_utils/collate_fn.py

@@ -0,0 +1,23 @@
+import torch
+
+
+# 对一个batch的数据处理
+def collate_fn(batch):
+    # 找出音频长度最长的
+    batch_sorted = sorted(batch, key=lambda sample: sample[0].size(0), reverse=True)
+    freq_size = batch_sorted[0][0].size(1)
+    max_freq_length = batch_sorted[0][0].size(0)
+    batch_size = len(batch_sorted)
+    # 以最大的长度创建0张量
+    features = torch.zeros((batch_size, max_freq_length, freq_size), dtype=torch.float32)
+    input_lens, labels = [], []
+    for x in range(batch_size):
+        tensor, label = batch[x]
+        seq_length = tensor.size(0)
+        # 将数据插入都0张量中，实现了padding
+        features[x, :seq_length, :] = tensor[:, :]
+        labels.append(label)
+        input_lens.append(seq_length)
+    labels = torch.tensor(labels, dtype=torch.int64)
+    input_lens = torch.tensor(input_lens, dtype=torch.int64)
+    return features, labels, input_lens

src/声源定位代码/audio-classification/macls/data_utils/featurizer.py

@@ -0,0 +1,132 @@
+import numpy as np
+import torch
+import torchaudio.compliance.kaldi as Kaldi
+from torch import nn
+from torchaudio.transforms import MelSpectrogram, Spectrogram, MFCC
+from loguru import logger
+
+
+class AudioFeaturizer(nn.Module):
+    """音频特征器
+
+    :param feature_method: 所使用的预处理方法
+    :type feature_method: str
+    :param use_hf_model: 是否使用HF上的Wav2Vec2类似模型提取音频特征
+    :type use_hf_model: bool
+    :param method_args: 预处理方法的参数
+    :type method_args: dict
+    """
+
+    def __init__(self, feature_method='MelSpectrogram', use_hf_model=False, method_args={}):
+        super().__init__()
+        self._method_args = method_args
+        self._feature_method = feature_method
+        self.use_hf_model = use_hf_model
+        if self.use_hf_model:
+            from transformers import AutoModel, AutoFeatureExtractor
+            # 判断是否使用GPU提取特征
+            use_gpu = torch.cuda.is_available() and method_args.get('use_gpu', True)
+            self.device = torch.device("cuda") if use_gpu else torch.device("cpu")
+            # 加载Wav2Vec2类似模型
+            self.processor = AutoFeatureExtractor.from_pretrained(feature_method)
+            self.feature_model = AutoModel.from_pretrained(feature_method).to(self.device)
+            logger.info(f'使用模型【{feature_method}】提取特征，使用【{self.device}】设备提取')
+            # 获取模型的输出通道数
+            inputs = self.processor(np.ones(16000 * 1, dtype=np.float32), sampling_rate=16000,
+                                    return_tensors="pt").to(self.device)
+            with torch.no_grad():
+                outputs = self.feature_model(**inputs)
+                self.output_channels = outputs.extract_features.shape[2]
+        else:
+            if feature_method == 'MelSpectrogram':
+                self.feat_fun = MelSpectrogram(**method_args)
+            elif feature_method == 'Spectrogram':
+                self.feat_fun = Spectrogram(**method_args)
+            elif feature_method == 'MFCC':
+                self.feat_fun = MFCC(**method_args)
+            elif feature_method == 'Fbank':
+                self.feat_fun = KaldiFbank(**method_args)
+            else:
+                raise Exception(f'预处理方法 {self._feature_method} 不存在!')
+            logger.info(f'使用【{feature_method}】提取特征')
+
+    def forward(self, waveforms, input_lens_ratio=None):
+        """从AudioSegment中提取音频特征
+
+        :param waveforms: Audio segment to extract features from.
+        :type waveforms: AudioSegment
+        :param input_lens_ratio: input length ratio
+        :type input_lens_ratio: tensor
+        :return: Spectrogram audio feature in 2darray.
+        :rtype: ndarray
+        """
+        if len(waveforms.shape) == 1:
+            waveforms = waveforms.unsqueeze(0)
+        if self.use_hf_model:
+            # 使用HF上的Wav2Vec2类似模型提取音频特征
+            if isinstance(waveforms, torch.Tensor):
+                waveforms = waveforms.numpy()
+            inputs = self.processor(waveforms, sampling_rate=16000,
+                                    return_tensors="pt").to(self.device)
+            with torch.no_grad():
+                outputs = self.feature_model(**inputs)
+                feature = outputs.extract_features.cpu().detach()
+        else:
+            # 使用普通方法提取音频特征
+            feature = self.feat_fun(waveforms)
+            feature = feature.transpose(2, 1)
+        # 归一化
+        feature = feature - feature.mean(1, keepdim=True)
+        if input_lens_ratio is not None:
+            # 对掩码比例进行扩展
+            input_lens = (input_lens_ratio * feature.shape[1])
+            mask_lens = torch.round(input_lens).long()
+            mask_lens = mask_lens.unsqueeze(1)
+            # 生成掩码张量
+            idxs = torch.arange(feature.shape[1], device=feature.device).repeat(feature.shape[0], 1)
+            mask = idxs < mask_lens
+            mask = mask.unsqueeze(-1)
+            # 对特征进行掩码操作
+            feature = torch.where(mask, feature, torch.zeros_like(feature))
+        return feature
+
+    @property
+    def feature_dim(self):
+        """返回特征大小
+
+        :return: 特征大小
+        :rtype: int
+        """
+        if self.use_hf_model:
+            return self.output_channels
+        if self._feature_method == 'MelSpectrogram':
+            return self._method_args.get('n_mels', 128)
+        elif self._feature_method == 'Spectrogram':
+            return self._method_args.get('n_fft', 400) // 2 + 1
+        elif self._feature_method == 'MFCC':
+            return self._method_args.get('n_mfcc', 40)
+        elif self._feature_method == 'Fbank':
+            return self._method_args.get('num_mel_bins', 23)
+        else:
+            raise Exception('没有{}预处理方法'.format(self._feature_method))
+
+
+class KaldiFbank(nn.Module):
+    def __init__(self, **kwargs):
+        super(KaldiFbank, self).__init__()
+        self.kwargs = kwargs
+
+    def forward(self, waveforms):
+        """
+        :param waveforms: [Batch, Length]
+        :return: [Batch, Feature, Length]
+        """
+        log_fbanks = []
+        for waveform in waveforms:
+            if len(waveform.shape) == 1:
+                waveform = waveform.unsqueeze(0)
+            log_fbank = Kaldi.fbank(waveform, **self.kwargs)
+            log_fbank = log_fbank.transpose(0, 1)
+            log_fbanks.append(log_fbank)
+        log_fbank = torch.stack(log_fbanks)
+        return log_fbank

src/声源定位代码/audio-classification/macls/data_utils/reader.py

@@ -0,0 +1,157 @@
+import random
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from tqdm import tqdm
+from yeaudio.audio import AudioSegment
+from yeaudio.augmentation import SpeedPerturbAugmentor, VolumePerturbAugmentor, NoisePerturbAugmentor, \
+    ReverbPerturbAugmentor, SpecAugmentor
+
+from macls.data_utils.featurizer import AudioFeaturizer
+
+
+class MAClsDataset(Dataset):
+    def __init__(self,
+                 data_list_path,
+                 audio_featurizer: AudioFeaturizer,
+                 max_duration=3,
+                 min_duration=0.5,
+                 mode='train',
+                 sample_rate=16000,
+                 aug_conf=None,
+                 use_dB_normalization=True,
+                 target_dB=-20):
+        """音频数据加载器
+
+        Args:
+            data_list_path: 包含音频路径和标签的数据列表文件的路径
+            audio_featurizer: 声纹特征提取器
+            max_duration: 最长的音频长度，大于这个长度会裁剪掉
+            min_duration: 过滤最短的音频长度
+            aug_conf: 用于指定音频增强的配置
+            mode: 数据集模式。在训练模式下，数据集可能会进行一些数据增强的预处理
+            sample_rate: 采样率
+            use_dB_normalization: 是否对音频进行音量归一化
+            target_dB: 音量归一化的大小
+        """
+        super(MAClsDataset, self).__init__()
+        assert mode in ['train', 'eval', 'extract_feature']
+        self.data_list_path = data_list_path
+        self.max_duration = max_duration
+        self.min_duration = min_duration
+        self.mode = mode
+        self._target_sample_rate = sample_rate
+        self._use_dB_normalization = use_dB_normalization
+        self._target_dB = target_dB
+        self.speed_augment = None
+        self.volume_augment = None
+        self.noise_augment = None
+        self.reverb_augment = None
+        self.spec_augment = None
+        # 获取特征器
+        self.audio_featurizer = audio_featurizer
+        # 获取特征裁剪的大小
+        self.max_feature_len = self.get_crop_feature_len()
+        # 获取数据列表
+        with open(self.data_list_path, 'r', encoding='utf-8') as f:
+            self.lines = f.readlines()
+        if mode == 'train' and aug_conf is not None:
+            # 获取数据增强器
+            self.get_augmentor(aug_conf)
+        # 评估模式下，数据列表需要排序
+        if self.mode == 'eval':
+            self.sort_list()
+
+    def __getitem__(self, idx):
+        # 分割数据文件路径和标签
+        data_path, label = self.lines[idx].replace('\n', '').split('\t')
+        # 如果后缀名为.npy的文件，那么直接读取
+        if data_path.endswith('.npy'):
+            feature = np.load(data_path)
+            if feature.shape[0] > self.max_feature_len:
+                crop_start = random.randint(0, feature.shape[0] - self.max_feature_len) if self.mode == 'train' else 0
+                feature = feature[crop_start:crop_start + self.max_feature_len, :]
+            feature = torch.tensor(feature, dtype=torch.float32)
+        else:
+            audio_path, label = self.lines[idx].strip().split('\t')
+            # 读取音频
+            audio_segment = AudioSegment.from_file(audio_path)
+            # 数据太短不利于训练
+            if self.mode == 'train' or self.mode == 'extract_feature':
+                if audio_segment.duration < self.min_duration:
+                    return self.__getitem__(idx + 1 if idx < len(self.lines) - 1 else 0)
+            # 音频增强
+            if self.mode == 'train':
+                audio_segment = self.augment_audio(audio_segment)
+            # 重采样
+            if audio_segment.sample_rate != self._target_sample_rate:
+                audio_segment.resample(self._target_sample_rate)
+            # 音量归一化
+            if self._use_dB_normalization:
+                audio_segment.normalize(target_db=self._target_dB)
+            # 裁剪需要的数据
+            if audio_segment.duration > self.max_duration:
+                audio_segment.crop(duration=self.max_duration, mode=self.mode)
+            samples = torch.tensor(audio_segment.samples, dtype=torch.float32)
+            feature = self.audio_featurizer(samples)
+            feature = feature.squeeze(0)
+        if self.mode == 'train' and self.spec_augment is not None:
+            feature = self.spec_augment(feature.cpu().numpy())
+            feature = torch.tensor(feature, dtype=torch.float32)
+        label = torch.tensor(int(label), dtype=torch.int64)
+        return feature, label
+
+    def __len__(self):
+        return len(self.lines)
+
+    # 获取特征裁剪的大小，对应max_duration音频提取特征后的长度
+    def get_crop_feature_len(self):
+        samples = torch.randn((1, self.max_duration * self._target_sample_rate))
+        feature = self.audio_featurizer(samples).squeeze(0)
+        freq_len = feature.size(0)
+        return freq_len
+
+    # 数据列表需要排序
+    def sort_list(self):
+        lengths = []
+        for line in tqdm(self.lines, desc=f"对列表[{self.data_list_path}]进行长度排序"):
+            # 分割数据文件路径和标签
+            data_path, _ = line.split('\t')
+            if data_path.endswith('.npy'):
+                feature = np.load(data_path)
+                length = feature.shape[0]
+                lengths.append(length)
+            else:
+                # 读取音频
+                audio_segment = AudioSegment.from_file(data_path)
+                length = audio_segment.duration
+                lengths.append(length)
+        # 对长度排序并获取索引
+        sorted_indexes = np.argsort(lengths)
+        self.lines = [self.lines[i] for i in sorted_indexes]
+
+    # 获取数据增强器
+    def get_augmentor(self, aug_conf):
+        if aug_conf.speed is not None:
+            self.speed_augment = SpeedPerturbAugmentor(**aug_conf.speed)
+        if aug_conf.volume is not None:
+            self.volume_augment = VolumePerturbAugmentor(**aug_conf.volume)
+        if aug_conf.noise is not None:
+            self.noise_augment = NoisePerturbAugmentor(**aug_conf.noise)
+        if aug_conf.reverb is not None:
+            self.reverb_augment = ReverbPerturbAugmentor(**aug_conf.reverb)
+        if aug_conf.spec_aug is not None:
+            self.spec_augment = SpecAugmentor(**aug_conf.spec_aug)
+
+    # 音频增强
+    def augment_audio(self, audio_segment):
+        if self.speed_augment is not None:
+            audio_segment = self.speed_augment(audio_segment)
+        if self.volume_augment is not None:
+            audio_segment = self.volume_augment(audio_segment)
+        if self.noise_augment is not None:
+            audio_segment = self.noise_augment(audio_segment)
+        if self.reverb_augment is not None:
+            audio_segment = self.reverb_augment(audio_segment)
+        return audio_segment

src/声源定位代码/audio-classification/macls/metric/metrics.py

@@ -0,0 +1,12 @@
+import numpy as np
+import torch
+
+
+# 计算准确率
+def accuracy(output, label):
+    output = torch.nn.functional.softmax(output, dim=-1)
+    output = output.data.cpu().numpy()
+    output = np.argmax(output, axis=1)
+    label = label.data.cpu().numpy()
+    acc = np.mean((output == label).astype(int))
+    return acc

src/声源定位代码/audio-classification/macls/optimizer/__init__.py

@@ -0,0 +1,32 @@
+import importlib
+
+from loguru import logger
+from torch.optim import *
+from .scheduler import WarmupCosineSchedulerLR
+from torch.optim.lr_scheduler import *
+
+__all__ = ['build_optimizer', 'build_lr_scheduler']
+
+
+def build_optimizer(params, configs):
+    use_optimizer = configs.optimizer_conf.get('optimizer', 'Adam')
+    optimizer_args = configs.optimizer_conf.get('optimizer_args', {})
+    optim = importlib.import_module(__name__)
+    optimizer = getattr(optim, use_optimizer)(params=params, **optimizer_args)
+    logger.info(f'成功创建优化方法：{use_optimizer}，参数为：{optimizer_args}')
+    return optimizer
+
+
+def build_lr_scheduler(optimizer, step_per_epoch, configs):
+    use_scheduler = configs.optimizer_conf.get('scheduler', 'WarmupCosineSchedulerLR')
+    scheduler_args = configs.optimizer_conf.get('scheduler_args', {})
+    if configs.optimizer_conf.scheduler == 'CosineAnnealingLR' and 'T_max' not in scheduler_args:
+        scheduler_args.T_max = int(configs.train_conf.max_epoch * 1.2) * step_per_epoch
+    if configs.optimizer_conf.scheduler == 'WarmupCosineSchedulerLR' and 'fix_epoch' not in scheduler_args:
+        scheduler_args.fix_epoch = configs.train_conf.max_epoch
+    if configs.optimizer_conf.scheduler == 'WarmupCosineSchedulerLR' and 'step_per_epoch' not in scheduler_args:
+        scheduler_args.step_per_epoch = step_per_epoch
+    optim = importlib.import_module(__name__)
+    scheduler = getattr(optim, use_scheduler)(optimizer=optimizer, **scheduler_args)
+    logger.info(f'成功创建学习率衰减：{use_scheduler}，参数为：{scheduler_args}')
+    return scheduler

src/声源定位代码/audio-classification/macls/optimizer/scheduler.py

@@ -0,0 +1,48 @@
+import math
+from typing import List
+
+
+class WarmupCosineSchedulerLR:
+    def __init__(
+            self,
+            optimizer,
+            min_lr,
+            max_lr,
+            warmup_epoch,
+            fix_epoch,
+            step_per_epoch
+    ):
+        self.optimizer = optimizer
+        assert min_lr <= max_lr
+        self.min_lr = min_lr
+        self.max_lr = max_lr
+        self.warmup_step = warmup_epoch * step_per_epoch
+        self.fix_step = fix_epoch * step_per_epoch
+        self.current_step = 0.0
+
+    def set_lr(self, ):
+        new_lr = self.clr(self.current_step)
+        for param_group in self.optimizer.param_groups:
+            param_group['lr'] = new_lr
+        return new_lr
+
+    def step(self, step=None):
+        if step is not None:
+            self.current_step = step
+        new_lr = self.set_lr()
+        self.current_step += 1
+        return new_lr
+
+    def clr(self, step):
+        if step < self.warmup_step:
+            return self.min_lr + (self.max_lr - self.min_lr) * \
+                (step / self.warmup_step)
+        elif self.warmup_step <= step < self.fix_step:
+            return self.min_lr + 0.5 * (self.max_lr - self.min_lr) * \
+                (1 + math.cos(math.pi * (step - self.warmup_step) /
+                              (self.fix_step - self.warmup_step)))
+        else:
+            return self.min_lr
+
+    def get_last_lr(self) -> List[float]:
+        return [self.clr(self.current_step)]

src/声源定位代码/audio-classification/macls/predict.py

@@ -0,0 +1,177 @@
+import os
+import sys
+from io import BufferedReader
+from typing import List
+
+import numpy as np
+import torch
+import yaml
+from loguru import logger
+from yeaudio.audio import AudioSegment
+from macls.data_utils.featurizer import AudioFeaturizer
+from macls.models import build_model
+from macls.utils.utils import dict_to_object, print_arguments, convert_string_based_on_type
+
+
+class MAClsPredictor:
+    def __init__(self,
+                 configs,
+                 model_path='models/CAMPPlus_Fbank/best_model/',
+                 use_gpu=True,
+                 overwrites=None,
+                 log_level="info"):
+        """声音分类预测工具
+
+        :param configs: 配置文件路径，或者模型名称，如果是模型名称则会使用默认的配置文件
+        :param model_path: 导出的预测模型文件夹路径
+        :param use_gpu: 是否使用GPU预测
+        :param overwrites: 覆盖配置文件中的参数，比如"train_conf.max_epoch=100"，多个用逗号隔开
+        :param log_level: 打印的日志等级，可选值有："debug", "info", "warning", "error"
+        """
+        if use_gpu:
+            assert (torch.cuda.is_available()), 'GPU不可用'
+            self.device = torch.device("cuda")
+        else:
+            os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+            self.device = torch.device("cpu")
+        self.log_level = log_level.upper()
+        logger.remove()
+        logger.add(sink=sys.stdout, level=self.log_level)
+        # 读取配置文件
+        if isinstance(configs, str):
+            # 获取当前程序绝对路径
+            absolute_path = os.path.dirname(__file__)
+            # 获取默认配置文件路径
+            config_path = os.path.join(absolute_path, f"configs/{configs}.yml")
+            configs = config_path if os.path.exists(config_path) else configs
+            with open(configs, 'r', encoding='utf-8') as f:
+                configs = yaml.load(f.read(), Loader=yaml.FullLoader)
+        self.configs = dict_to_object(configs)
+        # 覆盖配置文件中的参数
+        if overwrites:
+            overwrites = overwrites.split(",")
+            for overwrite in overwrites:
+                keys, value = overwrite.strip().split("=")
+                attrs = keys.split('.')
+                current_level = self.configs
+                for attr in attrs[:-1]:
+                    current_level = getattr(current_level, attr)
+                before_value = getattr(current_level, attrs[-1])
+                setattr(current_level, attrs[-1], convert_string_based_on_type(before_value, value))
+        # 打印配置信息
+        print_arguments(configs=self.configs)
+        # 获取特征器
+        self._audio_featurizer = AudioFeaturizer(feature_method=self.configs.preprocess_conf.feature_method,
+                                                 use_hf_model=self.configs.preprocess_conf.get('use_hf_model', False),
+                                                 method_args=self.configs.preprocess_conf.get('method_args', {}))
+        # 获取分类标签
+        with open(self.configs.dataset_conf.label_list_path, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+        self.class_labels = [l.replace('\n', '') for l in lines]
+        # 自动获取列表数量
+        if self.configs.model_conf.model_args.get('num_class', None) is None:
+            self.configs.model_conf.model_args.num_class = len(self.class_labels)
+        # 获取模型
+        self.predictor = build_model(input_size=self._audio_featurizer.feature_dim, configs=self.configs)
+        self.predictor.to(self.device)
+        # 加载模型
+        if os.path.isdir(model_path):
+            model_path = os.path.join(model_path, 'model.pth')
+        assert os.path.exists(model_path), f"{model_path} 模型不存在！"
+        if torch.cuda.is_available() and use_gpu:
+            model_state_dict = torch.load(model_path, weights_only=False)
+        else:
+            model_state_dict = torch.load(model_path, weights_only=False, map_location='cpu')
+        self.predictor.load_state_dict(model_state_dict)
+        logger.info(f"成功加载模型参数：{model_path}")
+        self.predictor.eval()
+
+    def _load_audio(self, audio_data, sample_rate=16000):
+        """加载音频
+        :param audio_data: 需要识别的数据，支持文件路径，文件对象，字节，numpy。如果是字节的话，必须是完整的字节文件
+        :param sample_rate: 如果传入的事numpy数据，需要指定采样率
+        :return: 识别的文本结果和解码的得分数
+        """
+        # 加载音频文件，并进行预处理
+        if isinstance(audio_data, str):
+            audio_segment = AudioSegment.from_file(audio_data)
+        elif isinstance(audio_data, BufferedReader):
+            audio_segment = AudioSegment.from_file(audio_data)
+        elif isinstance(audio_data, np.ndarray):
+            audio_segment = AudioSegment.from_ndarray(audio_data, sample_rate)
+        elif isinstance(audio_data, bytes):
+            audio_segment = AudioSegment.from_bytes(audio_data)
+        else:
+            raise Exception(f'不支持该数据类型，当前数据类型为：{type(audio_data)}')
+        # 重采样
+        if audio_segment.sample_rate != self.configs.dataset_conf.dataset.sample_rate:
+            audio_segment.resample(self.configs.dataset_conf.dataset.sample_rate)
+        # decibel normalization
+        if self.configs.dataset_conf.dataset.use_dB_normalization:
+            audio_segment.normalize(target_db=self.configs.dataset_conf.dataset.target_dB)
+        assert audio_segment.duration >= self.configs.dataset_conf.dataset.min_duration, \
+            f'音频太短，最小应该为{self.configs.dataset_conf.dataset.min_duration}s，当前音频为{audio_segment.duration}s'
+        return audio_segment
+
+    # 预测一个音频的特征
+    def predict(self,
+                audio_data,
+                sample_rate=16000):
+        """预测一个音频
+
+        :param audio_data: 需要识别的数据，支持文件路径，文件对象，字节，numpy。如果是字节的话，必须是完整并带格式的字节文件
+        :param sample_rate: 如果传入的事numpy数据，需要指定采样率
+        :return: 结果标签和对应的得分
+        """
+        # 加载音频文件，并进行预处理
+        input_data = self._load_audio(audio_data=audio_data, sample_rate=sample_rate)
+        input_data = torch.tensor(input_data.samples, dtype=torch.float32).unsqueeze(0)
+        audio_feature = self._audio_featurizer(input_data).to(self.device)
+        # 执行预测
+        output = self.predictor(audio_feature)
+        result = torch.nn.functional.softmax(output, dim=-1)[0]
+        result = result.data.cpu().numpy()
+        # 最大概率的label
+        lab = np.argsort(result)[-1]
+        score = result[lab]
+        return self.class_labels[lab], round(float(score), 5)
+
+    def predict_batch(self, audios_data: List, sample_rate=16000):
+        """预测一批音频的特征
+
+        :param audios_data: 需要识别的数据，支持文件路径，文件对象，字节，numpy。如果是字节的话，必须是完整并带格式的字节文件
+        :param sample_rate: 如果传入的事numpy数据，需要指定采样率
+        :return: 结果标签和对应的得分
+        """
+        audios_data1 = []
+        for audio_data in audios_data:
+            # 加载音频文件，并进行预处理
+            input_data = self._load_audio(audio_data=audio_data, sample_rate=sample_rate)
+            audios_data1.append(input_data.samples)
+        # 找出音频长度最长的
+        batch = sorted(audios_data1, key=lambda a: a.shape[0], reverse=True)
+        max_audio_length = batch[0].shape[0]
+        batch_size = len(batch)
+        # 以最大的长度创建0张量
+        inputs = np.zeros((batch_size, max_audio_length), dtype=np.float32)
+        input_lens_ratio = []
+        for x in range(batch_size):
+            tensor = audios_data1[x]
+            seq_length = tensor.shape[0]
+            # 将数据插入都0张量中，实现了padding
+            inputs[x, :seq_length] = tensor[:]
+            input_lens_ratio.append(seq_length / max_audio_length)
+        inputs = torch.tensor(inputs, dtype=torch.float32)
+        input_lens_ratio = torch.tensor(input_lens_ratio, dtype=torch.float32)
+        audio_feature = self._audio_featurizer(inputs, input_lens_ratio).to(self.device)
+        # 执行预测
+        output = self.predictor(audio_feature)
+        results = torch.nn.functional.softmax(output, dim=-1)
+        results = results.data.cpu().numpy()
+        labels, scores = [], []
+        for result in results:
+            lab = np.argsort(result)[-1]
+            score = result[lab]
+            labels.append(self.class_labels[lab])
+            scores.append(round(float(score), 5))
+        return labels, scores

src/声源定位代码/audio-classification/macls/trainer.py

@@ -0,0 +1,456 @@
+import os
+import platform
+import sys
+import time
+import uuid
+from datetime import timedelta
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import yaml
+from sklearn.metrics import confusion_matrix
+from torch.utils.data import DataLoader, RandomSampler
+from torch.utils.data.distributed import DistributedSampler
+from torchinfo import summary
+from tqdm import tqdm
+from loguru import logger
+from visualdl import LogWriter
+
+from macls.data_utils.collate_fn import collate_fn
+from macls.data_utils.featurizer import AudioFeaturizer
+from macls.data_utils.reader import MAClsDataset
+from macls.metric.metrics import accuracy
+from macls.models import build_model
+from macls.optimizer import build_optimizer, build_lr_scheduler
+from macls.utils.checkpoint import load_pretrained, load_checkpoint, save_checkpoint
+from macls.utils.utils import dict_to_object, plot_confusion_matrix, print_arguments, convert_string_based_on_type
+
+
+class MAClsTrainer(object):
+    def __init__(self,
+                 configs,
+                 use_gpu=True,
+                 data_augment_configs=None,
+                 num_class=None,
+                 overwrites=None,
+                 log_level="info"):
+        """声音分类训练工具类
+
+        :param configs: 配置文件路径，或者模型名称，如果是模型名称则会使用默认的配置文件
+        :param use_gpu: 是否使用GPU训练模型
+        :param data_augment_configs: 数据增强配置字典或者其文件路径
+        :param num_class: 分类大小，对应配置文件中的model_conf.model_args.num_class
+        :param overwrites: 覆盖配置文件中的参数，比如"train_conf.max_epoch=100"，多个用逗号隔开
+        :param log_level: 打印的日志等级，可选值有："debug", "info", "warning", "error"
+        """
+        if use_gpu:
+            assert (torch.cuda.is_available()), 'GPU不可用'
+            self.device = torch.device("cuda")
+        else:
+            os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+            self.device = torch.device("cpu")
+        self.use_gpu = use_gpu
+        self.log_level = log_level.upper()
+        logger.remove()
+        logger.add(sink=sys.stdout, level=self.log_level)
+        # 读取配置文件
+        if isinstance(configs, str):
+            # 获取当前程序绝对路径
+            absolute_path = os.path.dirname(__file__)
+            # 获取默认配置文件路径
+            config_path = os.path.join(absolute_path, f"configs/{configs}.yml")
+            configs = config_path if os.path.exists(config_path) else configs
+            with open(configs, 'r', encoding='utf-8') as f:
+                configs = yaml.load(f.read(), Loader=yaml.FullLoader)
+        self.configs = dict_to_object(configs)
+        if num_class is not None:
+            self.configs.model_conf.model_args.num_class = num_class
+        # 覆盖配置文件中的参数
+        if overwrites:
+            overwrites = overwrites.split(",")
+            for overwrite in overwrites:
+                keys, value = overwrite.strip().split("=")
+                attrs = keys.split('.')
+                current_level = self.configs
+                for attr in attrs[:-1]:
+                    current_level = getattr(current_level, attr)
+                before_value = getattr(current_level, attrs[-1])
+                setattr(current_level, attrs[-1], convert_string_based_on_type(before_value, value))
+        # 打印配置信息
+        print_arguments(configs=self.configs)
+        self.model = None
+        self.optimizer = None
+        self.scheduler = None
+        self.audio_featurizer = None
+        self.train_dataset = None
+        self.train_loader = None
+        self.test_dataset = None
+        self.test_loader = None
+        self.amp_scaler = None
+        # 读取数据增强配置文件
+        if isinstance(data_augment_configs, str):
+            with open(data_augment_configs, 'r', encoding='utf-8') as f:
+                data_augment_configs = yaml.load(f.read(), Loader=yaml.FullLoader)
+            print_arguments(configs=data_augment_configs, title='数据增强配置')
+        self.data_augment_configs = dict_to_object(data_augment_configs)
+        # 获取分类标签
+        with open(self.configs.dataset_conf.label_list_path, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+        self.class_labels = [l.replace('\n', '') for l in lines]
+        if platform.system().lower() == 'windows':
+            self.configs.dataset_conf.dataLoader.num_workers = 0
+            logger.warning('Windows系统不支持多线程读取数据，已自动关闭！')
+        if self.configs.preprocess_conf.get('use_hf_model', False):
+            self.configs.dataset_conf.dataLoader.num_workers = 0
+            logger.warning('使用HuggingFace模型不支持多线程进行特征提取，已自动关闭！')
+        self.max_step, self.train_step = None, None
+        self.train_loss, self.train_acc = None, None
+        self.train_eta_sec = None
+        self.eval_loss, self.eval_acc = None, None
+        self.test_log_step, self.train_log_step = 0, 0
+        self.stop_train, self.stop_eval = False, False
+
+    def __setup_dataloader(self, is_train=False):
+        """ 获取数据加载器
+
+        :param is_train: 是否获取训练数据
+        """
+        # 获取特征器
+        self.audio_featurizer = AudioFeaturizer(feature_method=self.configs.preprocess_conf.feature_method,
+                                                use_hf_model=self.configs.preprocess_conf.get('use_hf_model', False),
+                                                method_args=self.configs.preprocess_conf.get('method_args', {}))
+
+        dataset_args = self.configs.dataset_conf.get('dataset', {})
+        data_loader_args = self.configs.dataset_conf.get('dataLoader', {})
+        if is_train:
+            self.train_dataset = MAClsDataset(data_list_path=self.configs.dataset_conf.train_list,
+                                              audio_featurizer=self.audio_featurizer,
+                                              aug_conf=self.data_augment_configs,
+                                              mode='train',
+                                              **dataset_args)
+            # 设置支持多卡训练
+            train_sampler = RandomSampler(self.train_dataset)
+            if torch.cuda.device_count() > 1:
+                # 设置支持多卡训练
+                train_sampler = DistributedSampler(dataset=self.train_dataset)
+            self.train_loader = DataLoader(dataset=self.train_dataset,
+                                           collate_fn=collate_fn,
+                                           sampler=train_sampler,
+                                           **data_loader_args)
+        # 获取测试数据
+        data_loader_args.drop_last = False
+        dataset_args.max_duration = self.configs.dataset_conf.eval_conf.max_duration
+        data_loader_args.batch_size = self.configs.dataset_conf.eval_conf.batch_size
+        self.test_dataset = MAClsDataset(data_list_path=self.configs.dataset_conf.test_list,
+                                         audio_featurizer=self.audio_featurizer,
+                                         mode='eval',
+                                         **dataset_args)
+        self.test_loader = DataLoader(dataset=self.test_dataset,
+                                      collate_fn=collate_fn,
+                                      shuffle=False,
+                                      **data_loader_args)
+
+    def extract_features(self, save_dir='dataset/features', max_duration=100):
+        """ 提取特征保存文件
+
+        :param save_dir: 保存路径
+        :param max_duration: 提取特征的最大时长，避免过长显存不足，单位秒
+        """
+        self.audio_featurizer = AudioFeaturizer(feature_method=self.configs.preprocess_conf.feature_method,
+                                                use_hf_model=self.configs.preprocess_conf.get('use_hf_model', False),
+                                                method_args=self.configs.preprocess_conf.get('method_args', {}))
+        dataset_args = self.configs.dataset_conf.get('dataset', {})
+        dataset_args.max_duration = max_duration
+        data_loader_args = self.configs.dataset_conf.get('dataLoader', {})
+        data_loader_args.drop_last = False
+        for data_list in [self.configs.dataset_conf.train_list, self.configs.dataset_conf.test_list]:
+            test_dataset = MAClsDataset(data_list_path=data_list,
+                                        audio_featurizer=self.audio_featurizer,
+                                        mode='extract_feature',
+                                        **dataset_args)
+            test_loader = DataLoader(dataset=test_dataset,
+                                     collate_fn=collate_fn,
+                                     shuffle=False,
+                                     **data_loader_args)
+            save_data_list = data_list.replace('.txt', '_features.txt')
+            with open(save_data_list, 'w', encoding='utf-8') as f:
+                for features, labels, input_lens in tqdm(test_loader):
+                    for i in range(len(features)):
+                        feature, label, input_len = features[i], labels[i], input_lens[i]
+                        feature = feature.numpy()[:input_len]
+                        label = int(label)
+                        save_path = os.path.join(save_dir, str(label),
+                                                 f'{str(uuid.uuid4())}.npy').replace('\\', '/')
+                        os.makedirs(os.path.dirname(save_path), exist_ok=True)
+                        np.save(save_path, feature)
+                        f.write(f'{save_path}\t{label}\n')
+            logger.info(f'{data_list}列表中的数据已提取特征完成，新列表为：{save_data_list}')
+
+    def __setup_model(self, input_size, is_train=False):
+        """ 获取模型
+
+        :param input_size: 模型输入特征大小
+        :param is_train: 是否获取训练模型
+        """
+        # 自动获取列表数量
+        if self.configs.model_conf.model_args.get('num_class', None) is None:
+            self.configs.model_conf.model_args.num_class = len(self.class_labels)
+        # 获取模型
+        self.model = build_model(input_size=input_size, configs=self.configs)
+        self.model.to(self.device)
+        if self.log_level == "DEBUG" or self.log_level == "INFO":
+            # 打印模型信息，98是长度，这个取决于输入的音频长度
+            summary(self.model, input_size=(1, 98, input_size))
+        # 使用Pytorch2.0的编译器
+        if self.configs.train_conf.use_compile and torch.__version__ >= "2" and platform.system().lower() == 'windows':
+            self.model = torch.compile(self.model, mode="reduce-overhead")
+        # print(self.model)
+        # 获取损失函数
+        label_smoothing = self.configs.train_conf.get('label_smoothing', 0.0)
+        self.loss = torch.nn.CrossEntropyLoss(label_smoothing=label_smoothing)
+        if is_train:
+            if self.configs.train_conf.enable_amp:
+                self.amp_scaler = torch.GradScaler(init_scale=1024)
+            # 获取优化方法
+            self.optimizer = build_optimizer(params=self.model.parameters(), configs=self.configs)
+            # 学习率衰减函数
+            self.scheduler = build_lr_scheduler(optimizer=self.optimizer, step_per_epoch=len(self.train_loader),
+                                                configs=self.configs)
+
+    def __train_epoch(self, epoch_id, local_rank, writer, nranks=0):
+        """训练一个epoch
+
+        :param epoch_id: 当前epoch
+        :param local_rank: 当前显卡id
+        :param writer: VisualDL对象
+        :param nranks: 所使用显卡的数量
+        """
+        train_times, accuracies, loss_sum = [], [], []
+        start = time.time()
+        for batch_id, (features, label, input_len) in enumerate(self.train_loader):
+            if self.stop_train: break
+            if nranks > 1:
+                features = features.to(local_rank)
+                label = label.to(local_rank).long()
+            else:
+                features = features.to(self.device)
+                label = label.to(self.device).long()
+            # 执行模型计算，是否开启自动混合精度
+            with torch.autocast('cuda', enabled=self.configs.train_conf.enable_amp):
+                output = self.model(features)
+            # 计算损失值
+            los = self.loss(output, label)
+            # 是否开启自动混合精度
+            if self.configs.train_conf.enable_amp:
+                # loss缩放，乘以系数loss_scaling
+                scaled = self.amp_scaler.scale(los)
+                scaled.backward()
+            else:
+                los.backward()
+            # 是否开启自动混合精度
+            if self.configs.train_conf.enable_amp:
+                self.amp_scaler.unscale_(self.optimizer)
+                self.amp_scaler.step(self.optimizer)
+                self.amp_scaler.update()
+            else:
+                self.optimizer.step()
+            self.optimizer.zero_grad()
+
+            # 计算准确率
+            acc = accuracy(output, label)
+            accuracies.append(acc)
+            loss_sum.append(los.data.cpu().numpy())
+            train_times.append((time.time() - start) * 1000)
+            self.train_step += 1
+
+            # 多卡训练只使用一个进程打印
+            if batch_id % self.configs.train_conf.log_interval == 0 and local_rank == 0:
+                batch_id = batch_id + 1
+                # 计算每秒训练数据量
+                train_speed = self.configs.dataset_conf.dataLoader.batch_size / (
+                        sum(train_times) / len(train_times) / 1000)
+                # 计算剩余时间
+                self.train_eta_sec = (sum(train_times) / len(train_times)) * (self.max_step - self.train_step) / 1000
+                eta_str = str(timedelta(seconds=int(self.train_eta_sec)))
+                self.train_loss = sum(loss_sum) / len(loss_sum)
+                self.train_acc = sum(accuracies) / len(accuracies)
+                logger.info(f'Train epoch: [{epoch_id}/{self.configs.train_conf.max_epoch}], '
+                            f'batch: [{batch_id}/{len(self.train_loader)}], '
+                            f'loss: {self.train_loss:.5f}, accuracy: {self.train_acc:.5f}, '
+                            f'learning rate: {self.scheduler.get_last_lr()[0]:>.8f}, '
+                            f'speed: {train_speed:.2f} data/sec, eta: {eta_str}')
+                writer.add_scalar('Train/Loss', self.train_loss, self.train_log_step)
+                writer.add_scalar('Train/Accuracy', self.train_acc, self.train_log_step)
+                # 记录学习率
+                writer.add_scalar('Train/lr', self.scheduler.get_last_lr()[0], self.train_log_step)
+                train_times, accuracies, loss_sum = [], [], []
+                self.train_log_step += 1
+            start = time.time()
+            self.scheduler.step()
+
+    def train(self,
+              save_model_path='models/',
+              log_dir='log/',
+              max_epoch=None,
+              resume_model=None,
+              pretrained_model=None):
+        """
+        训练模型
+        :param save_model_path: 模型保存的路径
+        :param log_dir: 保存VisualDL日志文件的路径
+        :param max_epoch: 最大训练轮数，对应配置文件中的train_conf.max_epoch
+        :param resume_model: 恢复训练，当为None则不使用预训练模型
+        :param pretrained_model: 预训练模型的路径，当为None则不使用预训练模型
+        """
+        # 获取有多少张显卡训练
+        nranks = torch.cuda.device_count()
+        local_rank = 0
+        writer = None
+        if local_rank == 0:
+            # 日志记录器
+            writer = LogWriter(logdir=log_dir)
+
+        if nranks > 1 and self.use_gpu:
+            # 初始化NCCL环境
+            dist.init_process_group(backend='nccl')
+            local_rank = int(os.environ["LOCAL_RANK"])
+
+        # 获取数据
+        self.__setup_dataloader(is_train=True)
+        # 获取模型
+        self.__setup_model(input_size=self.audio_featurizer.feature_dim, is_train=True)
+        # 加载预训练模型
+        self.model = load_pretrained(model=self.model, pretrained_model=pretrained_model, use_gpu=self.use_gpu)
+        # 加载恢复模型
+        self.model, self.optimizer, self.amp_scaler, self.scheduler, last_epoch, best_acc = \
+            load_checkpoint(configs=self.configs, model=self.model, optimizer=self.optimizer,
+                            amp_scaler=self.amp_scaler, scheduler=self.scheduler, step_epoch=len(self.train_loader),
+                            save_model_path=save_model_path, resume_model=resume_model)
+
+        # 支持多卡训练
+        if nranks > 1 and self.use_gpu:
+            self.model.to(local_rank)
+            self.model = torch.nn.parallel.DistributedDataParallel(self.model, device_ids=[local_rank])
+        logger.info('训练数据：{}'.format(len(self.train_dataset)))
+
+        self.train_loss, self.train_acc = None, None
+        self.eval_loss, self.eval_acc = None, None
+        self.test_log_step, self.train_log_step = 0, 0
+        if local_rank == 0:
+            writer.add_scalar('Train/lr', self.scheduler.get_last_lr()[0], last_epoch)
+        if max_epoch is not None:
+            self.configs.train_conf.max_epoch = max_epoch
+        # 最大步数
+        self.max_step = len(self.train_loader) * self.configs.train_conf.max_epoch
+        self.train_step = max(last_epoch, 0) * len(self.train_loader)
+        # 开始训练
+        for epoch_id in range(last_epoch, self.configs.train_conf.max_epoch):
+            if self.stop_train: break
+            epoch_id += 1
+            start_epoch = time.time()
+            # 训练一个epoch
+            self.__train_epoch(epoch_id=epoch_id, local_rank=local_rank, writer=writer, nranks=nranks)
+            # 多卡训练只使用一个进程执行评估和保存模型
+            if local_rank == 0:
+                if self.stop_eval: continue
+                logger.info('=' * 70)
+                self.eval_loss, self.eval_acc = self.evaluate()
+                logger.info('Test epoch: {}, time/epoch: {}, loss: {:.5f}, accuracy: {:.5f}'.format(
+                    epoch_id, str(timedelta(seconds=(time.time() - start_epoch))), self.eval_loss, self.eval_acc))
+                logger.info('=' * 70)
+                writer.add_scalar('Test/Accuracy', self.eval_acc, self.test_log_step)
+                writer.add_scalar('Test/Loss', self.eval_loss, self.test_log_step)
+                self.test_log_step += 1
+                self.model.train()
+                # # 保存最优模型
+                if self.eval_acc >= best_acc:
+                    best_acc = self.eval_acc
+                    save_checkpoint(configs=self.configs, model=self.model, optimizer=self.optimizer,
+                                    amp_scaler=self.amp_scaler, save_model_path=save_model_path, epoch_id=epoch_id,
+                                    accuracy=self.eval_acc, best_model=True)
+                # 保存模型
+                save_checkpoint(configs=self.configs, model=self.model, optimizer=self.optimizer,
+                                amp_scaler=self.amp_scaler, save_model_path=save_model_path, epoch_id=epoch_id,
+                                accuracy=self.eval_acc)
+
+    def evaluate(self, resume_model=None, save_matrix_path=None):
+        """
+        评估模型
+        :param resume_model: 所使用的模型
+        :param save_matrix_path: 保存混合矩阵的路径
+        :return: 评估结果
+        """
+        if self.test_loader is None:
+            self.__setup_dataloader()
+        if self.model is None:
+            self.__setup_model(input_size=self.audio_featurizer.feature_dim)
+        if resume_model is not None:
+            if os.path.isdir(resume_model):
+                resume_model = os.path.join(resume_model, 'model.pth')
+            assert os.path.exists(resume_model), f"{resume_model} 模型不存在！"
+            model_state_dict = torch.load(resume_model, weights_only=False)
+            self.model.load_state_dict(model_state_dict)
+            logger.info(f'成功加载模型：{resume_model}')
+        self.model.eval()
+        if isinstance(self.model, torch.nn.parallel.DistributedDataParallel):
+            eval_model = self.model.module
+        else:
+            eval_model = self.model
+
+        accuracies, losses, preds, labels = [], [], [], []
+        with torch.no_grad():
+            for batch_id, (features, label, input_lens) in enumerate(tqdm(self.test_loader, desc='执行评估')):
+                if self.stop_eval: break
+                features = features.to(self.device)
+                label = label.to(self.device).long()
+                output = eval_model(features)
+                los = self.loss(output, label)
+                # 计算准确率
+                acc = accuracy(output, label)
+                accuracies.append(acc)
+                # 模型预测标签
+                label = label.data.cpu().numpy()
+                output = output.data.cpu().numpy()
+                pred = np.argmax(output, axis=1)
+                preds.extend(pred.tolist())
+                # 真实标签
+                labels.extend(label.tolist())
+                losses.append(los.data.cpu().numpy())
+        loss = float(sum(losses) / len(losses)) if len(losses) > 0 else -1
+        acc = float(sum(accuracies) / len(accuracies)) if len(accuracies) > 0 else -1
+        # 保存混合矩阵
+        if save_matrix_path is not None:
+            try:
+                cm = confusion_matrix(labels, preds)
+                plot_confusion_matrix(cm=cm, save_path=os.path.join(save_matrix_path, f'{int(time.time())}.png'),
+                                      class_labels=self.class_labels)
+            except Exception as e:
+                logger.error(f'保存混淆矩阵失败：{e}')
+        self.model.train()
+        return loss, acc
+
+    def export(self, save_model_path='models/', resume_model='models/EcapaTdnn_Fbank/best_model/'):
+        """
+        导出预测模型
+        :param save_model_path: 模型保存的路径
+        :param resume_model: 准备转换的模型路径
+        :return:
+        """
+        self.__setup_model(input_size=self.audio_featurizer.feature_dim)
+        # 加载预训练模型
+        if os.path.isdir(resume_model):
+            resume_model = os.path.join(resume_model, 'model.pth')
+        assert os.path.exists(resume_model), f"{resume_model} 模型不存在！"
+        model_state_dict = torch.load(resume_model)
+        self.model.load_state_dict(model_state_dict)
+        logger.info('成功恢复模型参数和优化方法参数：{}'.format(resume_model))
+        self.model.eval()
+        # 获取静态模型
+        infer_model = self.model.export()
+        infer_model_path = os.path.join(save_model_path,
+                                        f'{self.configs.use_model}_{self.configs.preprocess_conf.feature_method}',
+                                        'inference.pth')
+        os.makedirs(os.path.dirname(infer_model_path), exist_ok=True)
+        torch.jit.save(infer_model, infer_model_path)
+        logger.info("预测模型已保存：{}".format(infer_model_path))

src/声源定位代码/audio-classification/macls/utils/checkpoint.py

@@ -0,0 +1,162 @@
+import json
+import os
+import shutil
+
+import torch
+from loguru import logger
+from macls import __version__
+
+
+def load_pretrained(model, pretrained_model, use_gpu=True):
+    """加载预训练模型
+
+    :param model: 使用的模型
+    :param pretrained_model: 预训练模型路径
+    :param use_gpu: 模型是否使用GPU
+    :return: 加载的模型
+    """
+    # 加载预训练模型
+    if pretrained_model is None: return model
+    if os.path.isdir(pretrained_model):
+        pretrained_model = os.path.join(pretrained_model, 'model.pth')
+    assert os.path.exists(pretrained_model), f"{pretrained_model} 模型不存在！"
+    if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+        model_dict = model.module.state_dict()
+    else:
+        model_dict = model.state_dict()
+    if torch.cuda.is_available() and use_gpu:
+        model_state_dict = torch.load(pretrained_model, weights_only=False)
+    else:
+        model_state_dict = torch.load(pretrained_model, weights_only=False, map_location='cpu')
+    # 过滤不存在的参数
+    for name, weight in model_dict.items():
+        if name in model_state_dict.keys():
+            if list(weight.shape) != list(model_state_dict[name].shape):
+                logger.warning(f'{name} not used, shape {list(model_state_dict[name].shape)} '
+                               f'unmatched with {list(weight.shape)} in model.')
+                model_state_dict.pop(name, None)
+    # 加载权重
+    if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+        missing_keys, unexpected_keys = model.module.load_state_dict(model_state_dict, strict=False)
+    else:
+        missing_keys, unexpected_keys = model.load_state_dict(model_state_dict, strict=False)
+    if len(unexpected_keys) > 0:
+        logger.warning('Unexpected key(s) in state_dict: {}. '
+                       .format(', '.join('"{}"'.format(k) for k in unexpected_keys)))
+    if len(missing_keys) > 0:
+        logger.warning('Missing key(s) in state_dict: {}. '
+                       .format(', '.join('"{}"'.format(k) for k in missing_keys)))
+    logger.info('成功加载预训练模型：{}'.format(pretrained_model))
+    return model
+
+
+def load_checkpoint(configs, model, optimizer, amp_scaler, scheduler,
+                    step_epoch, save_model_path, resume_model):
+    """加载模型
+
+    :param configs: 配置信息
+    :param model: 使用的模型
+    :param optimizer: 使用的优化方法
+    :param amp_scaler: 使用的自动混合精度
+    :param scheduler: 使用的学习率调整策略
+    :param step_epoch: 每个epoch的step数量
+    :param save_model_path: 模型保存路径
+    :param resume_model: 恢复训练的模型路径
+    """
+    last_epoch1 = 0
+    accuracy1 = 0.
+
+    def load_model(model_path):
+        assert os.path.exists(os.path.join(model_path, 'model.pth')), "模型参数文件不存在！"
+        assert os.path.exists(os.path.join(model_path, 'optimizer.pth')), "优化方法参数文件不存在！"
+        state_dict = torch.load(os.path.join(model_path, 'model.pth'), weights_only=False)
+        if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+            model.module.load_state_dict(state_dict)
+        else:
+            model.load_state_dict(state_dict)
+        optimizer.load_state_dict(torch.load(os.path.join(model_path, 'optimizer.pth'), weights_only=False))
+        # 自动混合精度参数
+        if amp_scaler is not None and os.path.exists(os.path.join(model_path, 'scaler.pth')):
+            amp_scaler.load_state_dict(torch.load(os.path.join(model_path, 'scaler.pth')), weights_only=False)
+        with open(os.path.join(model_path, 'model.state'), 'r', encoding='utf-8') as f:
+            json_data = json.load(f)
+            last_epoch = json_data['last_epoch']
+            accuracy = json_data['accuracy']
+        logger.info('成功恢复模型参数和优化方法参数：{}'.format(model_path))
+        optimizer.step()
+        [scheduler.step() for _ in range(last_epoch * step_epoch)]
+        return last_epoch, accuracy
+
+    # 获取最后一个保存的模型
+    save_feature_method = configs.preprocess_conf.feature_method
+    if configs.preprocess_conf.get('use_hf_model', False):
+        save_feature_method = save_feature_method[:-1] if save_feature_method[-1] == '/' else save_feature_method
+        save_feature_method = os.path.basename(save_feature_method)
+    last_model_dir = os.path.join(save_model_path,
+                                  f'{configs.model_conf.model}_{save_feature_method}',
+                                  'last_model')
+    if resume_model is not None or (os.path.exists(os.path.join(last_model_dir, 'model.pth'))
+                                    and os.path.exists(os.path.join(last_model_dir, 'optimizer.pth'))):
+        if resume_model is not None:
+            last_epoch1, accuracy1 = load_model(resume_model)
+        else:
+            try:
+                # 自动获取最新保存的模型
+                last_epoch1, accuracy1 = load_model(last_model_dir)
+            except Exception as e:
+                logger.warning(f'尝试自动恢复最新模型失败，错误信息：{e}')
+    return model, optimizer, amp_scaler, scheduler, last_epoch1, accuracy1
+
+
+# 保存模型
+def save_checkpoint(configs, model, optimizer, amp_scaler, save_model_path, epoch_id,
+                    accuracy=0., best_model=False):
+    """保存模型
+
+    :param configs: 配置信息
+    :param model: 使用的模型
+    :param optimizer: 使用的优化方法
+    :param amp_scaler: 使用的自动混合精度
+    :param save_model_path: 模型保存路径
+    :param epoch_id: 当前epoch
+    :param accuracy: 当前准确率
+    :param best_model: 是否为最佳模型
+    """
+    if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    # 保存模型的路径
+    save_feature_method = configs.preprocess_conf.feature_method
+    if configs.preprocess_conf.get('use_hf_model', False):
+        save_feature_method = save_feature_method[:-1] if save_feature_method[-1] == '/' else save_feature_method
+        save_feature_method = os.path.basename(save_feature_method)
+    if best_model:
+        model_path = os.path.join(save_model_path,
+                                  f'{configs.model_conf.model}_{save_feature_method}', 'best_model')
+    else:
+        model_path = os.path.join(save_model_path,
+                                  f'{configs.model_conf.model}_{save_feature_method}', 'epoch_{}'.format(epoch_id))
+    os.makedirs(model_path, exist_ok=True)
+    # 保存模型参数
+    torch.save(optimizer.state_dict(), os.path.join(model_path, 'optimizer.pth'))
+    torch.save(state_dict, os.path.join(model_path, 'model.pth'))
+    # 自动混合精度参数
+    if amp_scaler is not None:
+        torch.save(amp_scaler.state_dict(), os.path.join(model_path, 'scaler.pth'))
+    with open(os.path.join(model_path, 'model.state'), 'w', encoding='utf-8') as f:
+        data = {"last_epoch": epoch_id, "accuracy": accuracy, "version": __version__,
+                "model": configs.model_conf.model, "feature_method": save_feature_method}
+        f.write(json.dumps(data, indent=4, ensure_ascii=False))
+    if not best_model:
+        last_model_path = os.path.join(save_model_path,
+                                       f'{configs.model_conf.model}_{save_feature_method}', 'last_model')
+        shutil.rmtree(last_model_path, ignore_errors=True)
+        shutil.copytree(model_path, last_model_path)
+        # 删除旧的模型
+        old_model_path = os.path.join(save_model_path,
+                                      f'{configs.model_conf.model}_{save_feature_method}',
+                                      'epoch_{}'.format(epoch_id - 3))
+        if os.path.exists(old_model_path):
+            shutil.rmtree(old_model_path)
+    logger.info('已保存模型：{}'.format(model_path))

src/声源定位代码/audio-classification/macls/utils/record.py

@@ -0,0 +1,31 @@
+import os
+
+import soundcard
+import soundfile
+
+
+class RecordAudio:
+    def __init__(self, channels=1, sample_rate=16000):
+        # 录音参数
+        self.channels = channels
+        self.sample_rate = sample_rate
+
+        # 获取麦克风
+        self.default_mic = soundcard.default_microphone()
+
+    def record(self, record_seconds=3, save_path=None):
+        """录音
+
+        :param record_seconds: 录音时间，默认3秒
+        :param save_path: 录音保存的路径，后缀名为wav
+        :return: 音频的numpy数据
+        """
+        print("开始录音......")
+        num_frames = int(record_seconds * self.sample_rate)
+        data = self.default_mic.record(samplerate=self.sample_rate, numframes=num_frames, channels=self.channels)
+        audio_data = data.squeeze()
+        print("录音已结束!")
+        if save_path is not None:
+            os.makedirs(os.path.dirname(save_path), exist_ok=True)
+            soundfile.write(save_path, data=data, samplerate=self.sample_rate)
+        return audio_data

src/声源定位代码/audio-classification/macls/utils/utils.py

@@ -0,0 +1,131 @@
+import distutils.util
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from loguru import logger
+
+
+def print_arguments(args=None, configs=None, title=None):
+    if args:
+        logger.info("----------- 额外配置参数 -----------")
+        for arg, value in sorted(vars(args).items()):
+            logger.info("%s: %s" % (arg, value))
+        logger.info("------------------------------------------------")
+    if configs:
+        title = title if title else "配置文件参数"
+        logger.info(f"----------- {title} -----------")
+        for arg, value in sorted(configs.items()):
+            if isinstance(value, dict):
+                logger.info(f"{arg}:")
+                for a, v in sorted(value.items()):
+                    if isinstance(v, dict):
+                        logger.info(f"\t{a}:")
+                        for a1, v1 in sorted(v.items()):
+                            logger.info("\t\t%s: %s" % (a1, v1))
+                    else:
+                        logger.info("\t%s: %s" % (a, v))
+            else:
+                logger.info("%s: %s" % (arg, value))
+        logger.info("------------------------------------------------")
+
+
+def add_arguments(argname, type, default, help, argparser, **kwargs):
+    type = distutils.util.strtobool if type == bool else type
+    argparser.add_argument("--" + argname,
+                           default=default,
+                           type=type,
+                           help=help + ' 默认: %(default)s.',
+                           **kwargs)
+
+
+class Dict(dict):
+    __setattr__ = dict.__setitem__
+    __getattr__ = dict.__getitem__
+
+
+def dict_to_object(dict_obj):
+    if not isinstance(dict_obj, dict):
+        return dict_obj
+    inst = Dict()
+    for k, v in dict_obj.items():
+        inst[k] = dict_to_object(v)
+    return inst
+
+
+def plot_confusion_matrix(cm, save_path, class_labels, show=False):
+    """
+    绘制混淆矩阵
+    @param cm: 混淆矩阵, 一个二维数组，表示预测结果与真实结果的混淆情况。
+    @param save_path: 保存路径, 字符串，指定混淆矩阵图像的保存位置。
+    @param class_labels: 类别名称, 一个列表，包含各个类别的名称。
+    @param show: 是否显示图像, 布尔值，控制是否在绘图窗口显示混淆矩阵图像。
+    """
+    # 检测类别名称是否包含中文，是则设置相应字体
+    s = ''.join(class_labels)
+    is_ascii = all(ord(c) < 128 for c in s)
+    if not is_ascii:
+        plt.rcParams['font.sans-serif'] = ['SimHei']
+        plt.rcParams['axes.unicode_minus'] = False
+
+    # 初始化绘图参数并绘制混淆矩阵
+    plt.figure(figsize=(12, 8), dpi=100)
+    np.set_printoptions(precision=2)
+    # 在混淆矩阵中绘制每个格子的概率值
+    ind_array = np.arange(len(class_labels))
+    x, y = np.meshgrid(ind_array, ind_array)
+    for x_val, y_val in zip(x.flatten(), y.flatten()):
+        c = cm[y_val][x_val] / (np.sum(cm[:, x_val]) + 1e-6)
+        # 忽略概率值太小的格子
+        if c < 1e-4: continue
+        plt.text(x_val, y_val, "%0.2f" % (c,), color='red', fontsize=15, va='center', ha='center')
+    m = np.sum(cm, axis=0) + 1e-6
+    plt.imshow(cm / m, interpolation='nearest', cmap=plt.cm.binary)
+    plt.title('Confusion Matrix' if is_ascii else '混合矩阵')
+    plt.colorbar()
+    # 设置类别标签
+    xlocations = np.array(range(len(class_labels)))
+    plt.xticks(xlocations, class_labels, rotation=90)
+    plt.yticks(xlocations, class_labels)
+    plt.ylabel('Actual label' if is_ascii else '实际标签')
+    plt.xlabel('Predict label' if is_ascii else '预测标签')
+
+    # 调整刻度标记位置，提高可视化效果
+    tick_marks = np.array(range(len(class_labels))) + 0.5
+    plt.gca().set_xticks(tick_marks, minor=True)
+    plt.gca().set_yticks(tick_marks, minor=True)
+    plt.gca().xaxis.set_ticks_position('none')
+    plt.gca().yaxis.set_ticks_position('none')
+    plt.grid(True, which='minor', linestyle='-')
+    plt.gcf().subplots_adjust(bottom=0.15)
+    # 保存图片
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    plt.savefig(save_path, format='png')
+    if show:
+        # 显示图片
+        plt.show()
+
+
+# 根据a的类型，将b转换为相应的类型
+def convert_string_based_on_type(a, b):
+    if isinstance(a, int):
+        try:
+            b = int(b)
+        except ValueError:
+            logger.error("无法将字符串转换为整数")
+    elif isinstance(a, float):
+        try:
+            b = float(b)
+        except ValueError:
+            logger.error("无法将字符串转换为浮点数")
+    elif isinstance(a, str):
+        return b
+    elif isinstance(a, bool):
+        b = b.lower() == 'true'
+    else:
+        try:
+            b = eval(b)
+        except Exception as e:
+            logger.exception("无法将字符串转换为其他类型，将忽略该参数类型转换")
+    return b

src/声源定位代码/audio-classification/record_audio.py

@@ -0,0 +1,14 @@
+import time
+
+from macls.utils.record import RecordAudio
+
+s = input('请输入你计划录音多少秒：')
+record_seconds = int(s)
+save_path = "dataset/save_audio/%s.wav" % str(int(time.time()*1000))
+
+record_audio = RecordAudio()
+input(f"按下回车键开机录音，录音{record_seconds}秒中：")
+record_audio.record(record_seconds=record_seconds,
+                    save_path=save_path)
+
+print('文件保存在：%s' % save_path)

src/声源定位代码/audio-classification/requirements.txt

@@ -0,0 +1,17 @@
+numpy>=1.19.2
+scipy>=1.6.3
+librosa>=0.9.1
+soundfile>=0.12.1
+soundcard>=0.4.2
+resampy>=0.2.2
+numba>=0.53.0
+pydub~=0.25.1
+matplotlib>=3.5.2
+pillow>=10.3.0
+tqdm>=4.66.3
+visualdl==2.5.3
+pyyaml>=5.4.1
+scikit-learn>=1.0.2
+torchinfo>=1.7.2
+loguru>=0.7.2
+yeaudio>=0.0.7

src/声源定位代码/audio-classification/setup.py

@@ -0,0 +1,54 @@
+import shutil
+
+from setuptools import setup, find_packages
+
+import macls
+
+VERSION = macls.__version__
+
+# 复制配置文件到项目目录下
+shutil.rmtree('./macls/configs/', ignore_errors=True)
+shutil.copytree('./configs/', './macls/configs/')
+
+
+def readme():
+    with open('README.md', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+
+def parse_requirements():
+    with open('./requirements.txt', encoding="utf-8") as f:
+        requirements = f.readlines()
+    return requirements
+
+
+if __name__ == "__main__":
+    setup(
+        name='macls',
+        packages=find_packages(),
+        package_data={'': ['configs/*']},
+        author='yeyupiaoling',
+        version=VERSION,
+        install_requires=parse_requirements(),
+        description='Audio Classification toolkit on Pytorch',
+        long_description=readme(),
+        long_description_content_type='text/markdown',
+        url='https://github.com/yeyupiaoling/AudioClassification-Pytorch',
+        download_url='https://github.com/yeyupiaoling/AudioClassification-Pytorch.git',
+        keywords=['audio', 'pytorch'],
+        classifiers=[
+            'Intended Audience :: Developers',
+            'License :: OSI Approved :: Apache Software License',
+            'Operating System :: OS Independent',
+            'Natural Language :: Chinese (Simplified)',
+            'Programming Language :: Python :: 3',
+            'Programming Language :: Python :: 3.5',
+            'Programming Language :: Python :: 3.6',
+            'Programming Language :: Python :: 3.7',
+            'Programming Language :: Python :: 3.8',
+            'Programming Language :: Python :: 3.9', 'Topic :: Utilities'
+        ],
+        license='Apache License 2.0',
+        ext_modules=[])
+    shutil.rmtree('./macls/configs/', ignore_errors=True)