Year: 2025
Type: article
Abstract: Unsupervised anomalous sound detection (ASD) models the normal sounds of machinery through classification operations, thereby identifying anomalies by quantifying deviations. Most recent approaches adopt depthwise separable modules from MobileNetV2. Extensive studies demonstrate that squeeze-and-excitation (SE) modules can enhance model fitting by dynamically weighting input features to adjust output distributions. However, we observe that conventional SE modules fail to adapt to the complex spectral textures of audio data. To address this, we propose an Audio Texture Attention (ATA) specifically designed for machine noise data, improving model robustness. Additionally, we integrate an LSTM layer and refine the temporal feature extraction architecture to strengthen the model’s sensitivity to sequential noise patterns. Experimental results on the DCASE 2020 Challenge Task 2 dataset show that our method achieves state-of-the-art performance, with AUC, pAUC, and mAUC scores of 96.15%, 90.58%, and 90.63%, respectively.
Source: Mathematics
DOI: 10.3390/math13172719
Authors: Yubo Su, Haolin Wang, Zhihao Xu, Chengxi Yin, Fucheng Chen, Zhaoguo Wang
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4413480784