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%.

翻译：本文提出了一种基于声音分离的无监督异常声音检测方法。在工厂环境中，背景噪声与非目标声音会掩盖所需机器声音，使得异常声音检测极具挑战性。因此，检测系统宜采用未混入背景噪声或非目的性声音的纯净信号。我们比较了所提方法的两个版本：一个将声音分离作为预处理步骤，另一个则基于分离式离群值暴露方法，利用两个分离声音之间的误差。基于正常声音与异常声音分离性能差异会影响检测结果的假设，两个版本均使用了针对特定产品类型的声音分离模型。实验结果表明，所提方法在所有机器ID上均提升了异常声音检测性能，最大提升幅度达39%。