在无标签情况下提升异常声音检测性能
Improvements of Discriminative Feature Space Training for Anomalous Sound Detection in Unlabeled Conditions
- 用多分辨率频谱图和新训练策略增强特征提取器
- 提出多种伪标签方法，显著改善无标签条件下的效果
- 适合工业故障检测等实际场景中的异常声音识别
在异常声音检测中，判别式方法已展现出优异性能。该方法通过正常声音的元信息标签构建判别特征空间，能有效反映机器声音差异并捕捉异常。然而当元信息标签缺失时，性能会显著下降。本文针对无标签条件，提出两种改进：一是增强特征提取器，采用多分辨率频谱图与新型训练策略；二是设计多种伪标签方法以有效训练特征提取器。实验结果表明，所提方法在无标签条件下显著提升了性能。
原文摘要 · Abstract (English)
In anomalous sound detection, the discriminative method has demonstrated superior performance. This approach constructs a discriminative feature space through the classification of the meta-information labels for normal sounds. This feature space reflects the differences in machine sounds and effectively captures anomalous sounds. However, its performance significantly degrades when the meta-information labels are missing. In this paper, we improve the performance of a discriminative method under unlabeled conditions by two approaches. First, we enhance the feature extractor to perform better under unlabeled conditions. Our enhanced feature extractor utilizes multi-resolution spectrograms with a new training strategy. Second, we propose various pseudo-labeling methods to effectively train the feature extractor. The experimental evaluations show that the proposed feature extractor and pseudo-labeling methods significantly improve performance under unlabeled conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。