# Low-complexity Attention-based Unsupervised Anomalous Sound Detection exploiting Separable Convolutions and Angular Loss

arXiv: https://arxiv.org/abs/2410.08919 · HTML: https://mlsanity.com/paper/2410.08919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [Multi-Channel Replay Speech Detection using Acoustic Maps](https://mlsanity.com/paper/2602.16399/) (2026)

## Abstract (arXiv)

In this work, a novel deep neural network, designed to enhance the efficiency and effectiveness of unsupervised sound anomaly detection, is presented. The proposed model exploits an attention module and separable convolutions to identify salient time-frequency patterns in audio data to discriminate between normal and anomalous sounds with reduced computational complexity. The approach is validated through extensive experiments using the Task 2 dataset of the DCASE 2020 challenge. Results demonstrate superior performance in terms of anomaly detection accuracy while having fewer parameters than state-of-the-art methods. Implementation details, code, and pre-trained models are available in https://github.com/michaelneri/unsupervised-audio-anomaly-detection.