Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training
Abstract
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.
Cite
@article{arxiv.2509.12845,
title = {Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training},
author = {Xin Fang and Guirui Zhong and Qing Wang and Fan Chu and Lei Wang and Mengui Qian and Mingqi Cai and Jiangzhao Wu and Jianqing Gao and Jun Du},
journal= {arXiv preprint arXiv:2509.12845},
year = {2025}
}
Comments
Copyright 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works