Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908546338127872 |
|---|---|
| author | Fang, Xin Zhong, Guirui Wang, Qing Chu, Fan Wang, Lei Qian, Mengui Cai, Mingqi Wu, Jiangzhao Gao, Jianqing Du, Jun |
| author_facet | Fang, Xin Zhong, Guirui Wang, Qing Chu, Fan Wang, Lei Qian, Mengui Cai, Mingqi Wu, Jiangzhao Gao, Jianqing Du, Jun |
| contents | Anomalous Sound Detection (ASD) is often formulated as a machine attribute classification task, a strategy necessitated by the common scenario where only normal data is available for training. However, the exhaustive collection of machine attribute labels is laborious and impractical. To address the challenge of missing attribute labels, this paper proposes an agglomerative hierarchical clustering method for the assignment of pseudo-attribute labels using representations derived from a domain-adaptive pre-trained model, which are expected to capture machine attribute characteristics. We then apply model adaptation to this pre-trained model through supervised fine-tuning for machine attribute classification, resulting in a new state-of-the-art performance. Evaluation on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge dataset demonstrates that our proposed approach yields significant performance gains, ultimately outperforming our previous top-ranking system in the challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training Fang, Xin Zhong, Guirui Wang, Qing Chu, Fan Wang, Lei Qian, Mengui Cai, Mingqi Wu, Jiangzhao Gao, Jianqing Du, Jun Sound Artificial Intelligence Anomalous Sound Detection (ASD) is often formulated as a machine attribute classification task, a strategy necessitated by the common scenario where only normal data is available for training. However, the exhaustive collection of machine attribute labels is laborious and impractical. To address the challenge of missing attribute labels, this paper proposes an agglomerative hierarchical clustering method for the assignment of pseudo-attribute labels using representations derived from a domain-adaptive pre-trained model, which are expected to capture machine attribute characteristics. We then apply model adaptation to this pre-trained model through supervised fine-tuning for machine attribute classification, resulting in a new state-of-the-art performance. Evaluation on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge dataset demonstrates that our proposed approach yields significant performance gains, ultimately outperforming our previous top-ranking system in the challenge. |
| title | Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12845 |