2023/05/25 by Kanta Shimonishi, Shimonishi, Kanta, Kota Dohi +3
Computer Science · #Anomaly Detection Techniques and Applications #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.15859
openalex publication_date 2023/05/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.