Self-supervised learning methods have achieved promising performance for anomalous sound detection (ASD) under domain shift, where the type of domain shift is considered in feature learning by incorporating section IDs. However, the attributes accompanying audio files under each section, such as machine operating conditions and noise types, have not been considered, although they are also crucial for characterizing domain shifts. In this paper, we present a hierarchical metadata information constrained self-supervised (HMIC) ASD method, where the hierarchical relation between section IDs and attributes is constructed, and used as constraints to obtain finer feature representation. In addition, we propose an attribute-group-center (AGC)-based method for calculating the anomaly score under the domain shift condition. Experiments are performed to demonstrate its improved performance over the state-of-the-art self-supervised methods in DCASE 2022 challenge Task 2.

翻译：自监督学习方法在域偏移条件下的异常声音检测中取得了显著成效，通过引入区域编号将域偏移类型纳入特征学习。然而，每个区域下音频文件附带的属性信息（如机器运行工况和噪声类型）尚未被充分考虑，尽管这些属性对表征域偏移同样至关重要。本文提出一种层级元数据信息约束自监督（HMIC）异常声音检测方法，通过构建区域编号与属性之间的层级关系，并将其作为约束条件获取更精细的特征表示。此外，我们提出基于属性组中心（AGC）的域偏移条件下异常分数计算方法。实验表明，该方法在DCASE 2022挑战赛任务2中相较现有最先进自监督方法具有更优性能。