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基于声学传感的属性驱动的不平衡补偿,用于检测无机器身份的异常声音
Yifan Zhou1, Yanhua Long1,2, Haoran Wei3
1Key Innovation Group of Digital Humanities Resource and Research, Shanghai Normal University, Shanghai 200234, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
本研究引入了一种用于异常声音检测 (ASD) 的新方法,在没有强大的机器识别先验时,使用声学传感器的弱属性标签. 这种方法提高了状态监控系统的稳定性和性能.
科学领域:
- 声学传感 声学传感 声学传感
- 机器学习 机器学习
- 状态监控 状态监控
背景情况:
- 异常声音检测 (ASD) 对于状态监测至关重要.
- 无监督的训练数据 (仅限正常声音) 给强大的ASD系统带来了挑战.
- 现有的歧视性模型依赖于强有力的先验知识 (例如机器ID),限制了现实世界的适用性.
研究的目的:
- 开发一个强大的ASD系统,使用来自声学传感器的弱属性标签.
- 克服模型的局限性,需要强大的先验知识.
- 在无监督和数据稀缺的场景中提高ASD性能.
主要方法:
- 使用不平衡和不一致的声学传感器属性 (例如速度,麦克风型号) 作为弱的先验.
- 训练一个属性分类器,对模型可训练性的不平衡补偿策略进行训练.
- 采用分数融合方法来提高异常检测的稳定性.
主要成果:
- 拟议的算法在DCASE2023挑战任务2中获得了第六位.
- 通过利用弱属性知识来证明有效的ASD性能.
- 展示了框架在缺乏强有力的先决方案的场景中的能力.
结论:
- 利用声学传感器数据属性作为弱先验提供了一个有效的ASD框架.
- 这种方法提供了一个可行的解决方案,用于在没有强大的先验的情况下进行稳健的状态监测.
- 开发的方法可以提高ASD系统在实际,现实世界的应用中的性能.
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