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arXiv · 2305.15859

Anomalous Sound Detection Based on Sound Separation

Kanta Shimonishi·Kota Dohi·Yohei Kawaguchi
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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%.

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Kanta Shimonishi, Kota Dohi, Yohei Kawaguchi. 2023-05-25. Anomalous Sound Detection Based on Sound Separation. https://arxiv.org/abs/2305.15859

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