Representation Learning Using Machine Attribute Information for Anomalous Sound Detection in Real Scenarios

In the previous Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge Task 2: Anomalous Sound Detection (ASD) for Machine Condition Monitoring, each machine has a variety of different section IDs, which are subsets of the machine type. Therefore, section ID classification is often used to learn the representation of machine sounds for ASD. However, in real scenarios, it is both time-consuming and laborious for each machine to record data with multiple different section IDs. As such, the Task 2 of DCASE 2023 Challenge only includes one section ID for each machine, with the attribute information reflecting the machine’s working status and environment for recording. To this end, machine sound representations for ASD can be learned through the proxy task of two-stage multi-attribute classification. Specifically, the sounds of all machines are first used to pre-train a general attribute classification model. This model is then fine-tuned to obtain an attribute classification model specific to each machine, with a classification head established for each attribute that affects the acoustic characteristics of the machine in a multi-task learning framework. At the same time, data augmentation is used to improve the generalization capability caused by the limited amount of data in actual scenarios. Our approach demonstrates commendable performance on the Task 2 of DCASE 2023 Challenge. We further illustrate the effectiveness of our method through visual analysis.