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迁移下的异常声音检测（ASD）中取得了显著成效，通过结合分区ID在特征学习中考虑了域迁移类型。然而，每个分区下音频文件附带的属性（如机器运行条件和噪声类型）尚未被考虑，尽管它们对于刻画域迁移同样至关重要。本文提出了一种层次化元信息约束的自监督（HMIC）ASD方法，该方法构建了分区ID与属性之间的层次关系，并将其作为约束以获得更精细的特征表示。此外，我们提出了一种基于属性组中心（AGC）的方法，用于在域迁移条件下计算异常分数。实验表明，该方法在DCASE 2022挑战赛任务2中相较于最先进的自监督方法具有更优性能。