MDAM: Multi-Dimensional Attention Module for Anomalous Sound Detection

Unsupervised anomaly sound detection (ASD) is a challenging task that involves training a model to differentiate between normal and abnormal sounds in an unsupervised manner. The difficulty of the task increases when there are acoustic differences (domain shift) between the training and testing datasets. To address these issues, this paper proposes a state-of-the-art ASD model based on self-supervised learning. Firstly, we designed an effective attention module called the Multi-Dimensional Attention Module (MDAM). Given a shallow feature map of sound, this module infers attention along three independent dimensions: time, frequency, and channel. It focuses on specific frequency bands that contain discriminative information and time frames relevant to semantics, thereby enhancing the representation learning capability of the network model. MDAM is a lightweight and versatile module that can be seamlessly integrated into any CNN-based ASD model. Secondly, we propose a simple domain generalization method that increases domain diversity by blending the feature representations of different domain data, thereby mitigating domain shift. Finally, we validate the effectiveness of the proposed methods on DCASE 2022 Task 2 and DCASE 2023 Task 2.