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

arXiv: https://arxiv.org/abs/2308.14063 · HTML: https://mlsanity.com/paper/2308.14063/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2023.

## Built on by (most cited first, 3 of 3)

- [A Novel Transfer Learning Method Utilizing Acoustic and Vibration Signals for Rotating Machinery Fault Diagnosis](https://mlsanity.com/paper/2310.14796/) (2023)
- [ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection](https://mlsanity.com/paper/2509.02471/) (2025)
- [Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection](https://mlsanity.com/paper/2509.13853/) (2025)

## Abstract (arXiv)

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.