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Different machines can exhibit diverse frequency patterns in their emittedsound. This feature has been recently explored in anomaly sound detection andreached state-of-the-art performance. However, existing methods rely on themanual or empirical determination of the frequency filter by observing theeffective frequency range in the training data, which may be impractical forgeneral application. This paper proposes an anomalous sound detection methodusing self-attention-based frequency pattern analysis and spectral-temporalinformation fusion. Our experiments demonstrate that the self-attention moduleautomatically and adaptively analyses the effective frequencies of a machinesound and enhances that information in the spectral feature representation.With spectral-temporal information fusion, the obtained audio featureeventually improves the anomaly detection performance on the DCASE 2020Challenge Task 2 dataset.

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