Abstract
This paper proposes an unsupervised anomalous sound detection method using sound separation. In factory environments, background noise and non-objective sounds obscure desired machine sounds, making it challenging to detect anomalous sounds. Therefore, using sounds not mixed with background noise or non-purpose sounds in the detection system is desirable. We compared two versions of our proposed method, one using sound separation as a pre-processing step and the other using separation-based outlier exposure that uses the error between two separated sounds. Based on the assumption that differences in separation performance between normal and anomalous sounds affect detection results, a sound separation model specific to a particular product type was used in both versions. Experimental results indicate that the proposed method improved anomalous sound detection performance for all Machine IDs, achieving a maximum improvement of 39%.
Abstract (translated)
这篇文章提出了一种使用声音分离 unsupervised 的方法来解决异常声音检测问题。在工厂环境中，背景噪音和非客观的声音会掩盖想要的机器声音，这使得检测异常声音变得困难。因此，在检测系统中不使用与背景噪音或非目的声音混合的声音是理想的。我们比较了我们提出的方法的两个版本，一个使用声音分离作为预处理步骤，另一个使用基于分离的异常检测突出显示，使用了分离前后声音之间的误差。根据假设，正常和异常声音分离性能的差异会影响检测结果，因此我们在两个版本中都使用了特定于某一产品类型的声分离模型。实验结果表明， proposed 方法对所有机器编号的异常声音检测性能都做出了最大 39% 的提高。
URL
https://arxiv.org/abs/2305.15859
