- 了解機器學習概念和模型訓練
- 具備Python編程和數據預處理經驗
- 熟悉數位音頻基礎知識
目標受眾
- 數據科學家
- 機器學習工程師
- 音頻信號處理領域的研究人員和開發者
Audio Classification and Event Detection with ML 是一門技術課程，專注於構建機器學習模型，用於分類音頻並在現實環境中檢測聲音事件。
這門由講師指導的培訓（線上或線下）面向中高級數據專業人員，他們希望應用機器學習技術來分析和分類音頻數據，用於公共安全、製造、智慧城市和多媒體分析等領域。
培訓結束後，參與者將能夠：
- 了解如何使用機器學習對聲音事件進行建模和分類。
- 使用MFCC和頻譜圖等特徵提取技術預處理音頻數據。
- 構建、訓練和評估用於音頻分類和事件檢測的模型。
- 在企業或嵌入式環境中部署機器學習模型，用於實時或批處理音頻處理。
課程形式
- 互動式講座和討論。
- 大量練習和實踐。
- 在實驗室環境中進行實踐操作。
課程定制選項
- 如需為本課程定制培訓，請聯繫我們安排。
音頻分類基礎
- 聲音事件類型：環境、機械、人為
- 應用場景概述：監控、監測、自動化
- 音頻分類 vs 檢測 vs 分割
音頻數據與特徵提取
- 音頻文件類型與格式
- 採樣率、窗口化、幀大小考慮
- 提取MFCCs、色度特徵、梅爾頻譜圖
數據準備與註釋
- UrbanSound8K、ESC-50及自定義數據集
- 標記聲音事件與時間邊界
- 平衡數據集與音頻增強
構建音頻分類模型
- 使用卷積神經網絡（CNNs）處理音頻
- 模型輸入：原始波形 vs 特徵
- 損失函數、評估指標與過擬合
事件檢測與時間定位
- 基於幀與基於片段的檢測策略
- 使用閾值與平滑處理後期檢測
- 在音頻時間線上可視化預測結果
高級主題與實時處理
- 低數據場景下的遷移學習
- 使用TensorFlow Lite或ONNX部署模型
- 流媒體音頻處理與延遲考慮
項目開發與應用場景
- 設計完整流程：從數據攝取到分類
- 開發監控、質量控制或監測的概念驗證
- 日誌記錄、警報與儀表板或API的集成
總結與下一步
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