提出分层解耦方法，提升异常声音检测在域偏移下的鲁棒性。
Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift
- 用梯度反转分离领域相关与无关特征，增强表征能力。
- 引入分层结构，利用设备编号等元信息学习细粒度领域特征。
- 在DCASE 2022数据集上显著提升域偏移场景下的检测性能。
异常声音检测（ASD）在域偏移下面临挑战，因目标域机器声音受运行条件影响，与源域差异显著。现有方法常依赖域分类器提升性能，却忽略域无关信息的影响，导致模型难以清晰区分域间差异，削弱正常与异常声音的辨别能力。本文提出基于梯度反转的分层特征解耦（GRHD）方法，通过梯度反转将域相关特征与域无关特征分离，获得更鲁棒的特征表示。同时，利用设备编号、声音属性等元信息构建分层结构，引导模型学习细粒度的领域特定特征。在DCASE 2022挑战任务2的数据集上实验表明，该方法在域偏移条件下显著提升了异常声音检测性能。
原文摘要 · Abstract (English)
Anomalous sound detection (ASD) encounters difficulties with domain shift, where the sounds of machines in target domains differ significantly from those in source domains due to varying operating conditions. Existing methods typically employ domain classifiers to enhance detection performance, but they often overlook the influence of domain-unrelated information. This oversight can hinder the model's ability to clearly distinguish between domains, thereby weakening its capacity to differentiate normal from abnormal sounds. In this paper, we propose a Gradient Reversal-based Hierarchical feature Disentanglement (GRHD) method to address the above challenge. GRHD uses gradient reversal to separate domain-related features from domain-unrelated ones, resulting in more robust feature representations. Additionally, the method employs a hierarchical structure to guide the learning of fine-grained, domain-specific features by leveraging available metadata, such as section IDs and machine sound attributes. Experimental results on the DCASE 2022 Challenge Task 2 dataset demonstrate that the proposed method significantly improves ASD performance under domain shift.
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