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.Comment: Published in INTERSPEECH 202