Low-Complexity Attention-Based Unsupervised Anomalous Sound Detection Exploiting Separable Convolutions and Angular Loss
Year: 2024
Type: article
Abstract: In this letter, 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.
Source: IEEE Sensors Letters
Authors: Michael Neri, Marco Carli
Institution: Roma Tre University
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,495
OpenAlex ID: W4403390258