Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training
This work addresses a practical bottleneck in ASD for applications like industrial monitoring, though it is incremental as it builds on existing pre-training and fine-tuning methods.
The paper tackled the problem of missing machine attribute labels in Anomalous Sound Detection by proposing an agglomerative hierarchical clustering method to assign pseudo-attribute labels using domain-adaptive pre-trained representations, resulting in a new state-of-the-art performance on the DCASE 2025 Challenge dataset.
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.