Anomalous Sound Detection using Spectral-Temporal Information Fusion

TL;DR

This paper introduces a spectral-temporal fusion self-supervised approach for unsupervised anomalous sound detection, significantly enhancing stability and accuracy across different machine types compared to existing methods.

Contribution

It proposes a novel spectral-temporal fusion method that improves detection stability and performance consistency in unsupervised anomalous sound detection for individual machines.

Findings

Achieved over 81% in minimum AUC for four machine types.

Improved detection performance by up to 31.79% over state-of-the-art.

Enhanced average AUC across all machine types.

Abstract

Unsupervised anomalous sound detection aims to detect unknown abnormal sounds of machines from normal sounds. However, the state-of-the-art approaches are not always stable and perform dramatically differently even for machines of the same type, making it impractical for general applications. This paper proposes a spectral-temporal fusion based self-supervised method to model the feature of the normal sound, which improves the stability and performance consistency in detection of anomalous sounds from individual machines, even of the same type. Experiments on the DCASE 2020 Challenge Task 2 dataset show that the proposed method achieved 81.39\%, 83.48\%, 98.22\% and 98.83\% in terms of the minimum AUC (worst-case detection performance amongst individuals) in four types of real machines (fan, pump, slider and valve), respectively, giving 31.79\%, 17.78\%, 10.42\% and 21.13\% improvement compared to the state-of-the-art method, i.e., Glow\_Aff. Moreover, the proposed method has improved AUC (average performance of individuals) for all the types of machines in the dataset. The source codes are available at https://github.com/liuyoude/STgram_MFN