Anomalous Sound Detection Using Self-Attention-Based Frequency Pattern Analysis of Machine Sounds

TL;DR

This paper introduces a self-attention-based method for anomaly sound detection that adaptively analyzes frequency patterns and fuses spectral-temporal features, improving detection performance without manual frequency filter tuning.

Contribution

It proposes a novel self-attention mechanism for automatic frequency pattern analysis and spectral-temporal fusion in anomaly sound detection, addressing limitations of manual filter selection.

Findings

Automatic frequency analysis enhances detection accuracy.

Spectral-temporal fusion improves anomaly detection performance.

Method achieves state-of-the-art results on DCASE 2020 dataset.

Abstract

Different machines can exhibit diverse frequency patterns in their emitted sound. This feature has been recently explored in anomaly sound detection and reached state-of-the-art performance. However, existing methods rely on the manual or empirical determination of the frequency filter by observing the effective frequency range in the training data, which may be impractical for general application. This paper proposes an anomalous sound detection method using self-attention-based frequency pattern analysis and spectral-temporal information fusion. Our experiments demonstrate that the self-attention module automatically and adaptively analyses the effective frequencies of a machine sound and enhances that information in the spectral feature representation. With spectral-temporal information fusion, the obtained audio feature eventually improves the anomaly detection performance on the DCASE 2020 Challenge Task 2 dataset.