An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918128336764928 |
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| author | Zhong, Guirui Wang, Qing Du, Jun Wang, Lei Cai, Mingqi Fang, Xin |
| author_facet | Zhong, Guirui Wang, Qing Du, Jun Wang, Lei Cai, Mingqi Fang, Xin |
| contents | Anomalous Sound Detection (ASD) aims at identifying anomalous sounds from machines and has gained extensive research interests from both academia and industry. However, the uncertainty of anomaly location and much redundant information such as noise in machine sounds hinder the improvement of ASD system performance. This paper proposes a novel audio feature of filter banks with evenly distributed intervals, ensuring equal attention to all frequency ranges in the audio, which enhances the detection of anomalies in machine sounds. Moreover, based on pre-trained models, this paper presents a parameter-free feature enhancement approach to remove redundant information in machine audio. It is believed that this parameter-free strategy facilitates the effective transfer of universal knowledge from pre-trained tasks to the ASD task during model fine-tuning. Evaluation results on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge dataset demonstrate significant improvements in ASD performance with our proposed methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models Zhong, Guirui Wang, Qing Du, Jun Wang, Lei Cai, Mingqi Fang, Xin Sound Machine Learning Audio and Speech Processing Anomalous Sound Detection (ASD) aims at identifying anomalous sounds from machines and has gained extensive research interests from both academia and industry. However, the uncertainty of anomaly location and much redundant information such as noise in machine sounds hinder the improvement of ASD system performance. This paper proposes a novel audio feature of filter banks with evenly distributed intervals, ensuring equal attention to all frequency ranges in the audio, which enhances the detection of anomalies in machine sounds. Moreover, based on pre-trained models, this paper presents a parameter-free feature enhancement approach to remove redundant information in machine audio. It is believed that this parameter-free strategy facilitates the effective transfer of universal knowledge from pre-trained tasks to the ASD task during model fine-tuning. Evaluation results on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge dataset demonstrate significant improvements in ASD performance with our proposed methods. |
| title | An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.15334 |