Adaptive Prototype Learning for Anomalous Sound Detection with Partially Known Attributes

Adapting pre-trained models has become the dominant approach for anomalous sound detection (ASD), where classifying the attributes of machine working status is commonly chosen as the deputy task for fine-tuning. However, attributes might be intractable to collect for some machines, causing the label to bear mixed granularity and thus deprecating the ASD performance. Therefore, we propose an adaptive proto-type learning scheme for fine-tuning pre-trained models, which adaptively scales coarse-grained labels to sub-centers so as to keep consistency with fine-grained labels. To deal with domain shift, we employ SMOTE to over-sample the prototypes of the target domain. The experiment on the DCASE 2024 ASD dataset demonstrates the efficacy of the proposed scheme, setting up a new milestone of 65.01% on both sets and outperforming the best system of the challenge. A detailed ablation study is also conducted to validate the effectiveness.