交叉引用自我训练网络用于在音频混合物中的声音事件检测
Sangwook Park1, David K Han2, Mounya Elhilali3
1Department of Electronic Engineering, Gangneung-Wonju National University, Gangneung, 25457 South Korea.
概括
本研究引入了一种半监督的声音事件检测方法,利用学生-教师方案从未标记的数据生成伪标签. 这种方法可以显著提高性能,而无需大量的手动标签.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 声音事件检测对于音频标签至关重要,识别声音类别和时间边界.
- 深度神经网络具有先进的声音事件检测,但需要广泛的标记数据.
- 监督方法占主导地位,需要昂贵的数据收集和注释.
研究的目的:
- 提出一种半监督的声音事件检测方法,以减少对标记数据的依赖.
- 探索一个学生-教师方案,平衡自我训练和交叉训练,用于伪标签的生成.
- 研究后处理技术,以提高声音事件检测性能.
主要方法:
- 一种半监督的方法,使用学生-教师模型从无监督数据生成伪标签.
- 实施一个在学生-教师框架内平衡自我培训和交叉培训的计划.
- 应用后处理方法来完善从网络预测中提取声音事件间隔的方法.
主要成果:
- 拟议的半监督方法在DCASE2020挑战数据集上取得了显著的性能改进.
- 对DESED数据库的验证和公共评估集的评估显示出了卓越的结果.
- 该方法在半监督的声音事件检测方面超过了现有的最先进系统.
结论:
- 开发的半监督方法为声音事件检测的完全监督方法提供了有效的替代方案.
- 学生-教师方案与后处理相结合,可提高检测准确度,同时最大限度地减少标签工作.
- 这项研究有助于更高效和可扩展的声音事件检测系统.
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