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