- 了解机器学习概念和模型训练
- 具备Python编程和数据预处理经验
- 熟悉数字音频基础知识
受众
- 数据科学家
- 机器学习工程师
- 音频信号处理领域的研究人员和开发者
Audio Classification and Event Detection with ML 是一门技术课程，专注于构建机器学习模型，以对音频进行分类并检测现实环境中的声音事件。
本课程由讲师指导，提供线上或线下培训，面向中级到高级数据专业人士，他们希望应用机器学习技术来分析和分类音频数据，用于公共安全、制造、智慧城市和多媒体分析等领域。
在培训结束时，参与者将能够：
- 了解如何使用机器学习对声音事件进行建模和分类。
- 使用MFCC和频谱图等特征提取技术预处理音频数据。
- 构建、训练和评估音频分类和事件检测模型。
- 在企业或嵌入式环境中部署机器学习模型，用于实时或批处理的音频处理。
课程形式
- 互动讲座和讨论。
- 大量练习和实践。
- 在实时实验室环境中进行动手操作。
课程定制选项
- 如需为本课程申请定制培训，请联系我们安排。
音频分类基础
- 声音事件类型：环境、机械、人为
- 应用场景概述：监控、监测、自动化
- 音频分类 vs 检测 vs 分割
音频数据与特征提取
- 音频文件类型与格式
- 采样率、窗口化、帧大小考虑
- 提取MFCCs、色度特征、梅尔频谱图
数据准备与注释
- UrbanSound8K、ESC-50及自定义数据集
- 标记声音事件与时间边界
- 平衡数据集与音频增强
构建音频分类模型
- 使用卷积神经网络（CNNs）处理音频
- 模型输入：原始波形 vs 特征
- 损失函数、评估指标与过拟合
事件检测与时间定位
- 基于帧与基于片段的检测策略
- 使用阈值与平滑处理后期检测
- 在音频时间线上可视化预测结果
高级主题与实时处理
- 低数据场景下的迁移学习
- 使用TensorFlow Lite或ONNX部署模型
- 流媒体音频处理与延迟考虑
项目开发与应用场景
- 设计完整流程：从数据摄取到分类
- 开发监控、质量控制或监测的概念验证
- 日志记录、警报与仪表板或API的集成
总结与下一步
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