用自适应预训练模型生成伪属性标签，提升异常声音检测效果
Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training
- 通过聚类为无标签数据生成伪属性标签
- 在DCASE 2025数据集上超越此前顶尖系统
- 适合缺乏标注数据的工业异常检测场景
异常声音检测（ASD）通常被建模为机器属性分类任务，这源于训练时仅能获取正常数据的现实。然而，全面收集机器属性标签耗时且不切实际。为此，本文提出一种层次聚类方法，利用领域自适应预训练模型生成的表征来分配伪属性标签，这些表征预期能捕捉机器属性特征。随后通过有监督微调对预训练模型进行适配，实现机器属性分类，最终达到新的最优性能。在DCASE 2025挑战赛数据集上的评估表明，所提方法取得显著性能提升，最终超越此前排名领先的系统。
原文摘要 · Abstract (English)
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.
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