# Anomalous Sound Detection Based on Sound Separation

arXiv: https://arxiv.org/abs/2305.15859 · HTML: https://mlsanity.com/paper/2305.15859/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2023.

## Built on by (most cited first, 1 of 1)

- [Activity-Guided Industrial Anomalous Sound Detection against Interferences](https://mlsanity.com/paper/2409.01885/) (2024)

## Abstract (arXiv)

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%.