Angular Margin Loss-Driven and Distance-Aware Multi-Sample Mixing for Self-Supervised Anomalous Sound Detection

The mixup strategy combined with Angular Margin Loss (AML) has shown promising results in Anomalous Sound Detection (ASD). However, a comprehensive theoretical understanding of how mixup facilitates normal distribution modeling within the AML framework remains absent, which impedes further optimization and application in ASD. Through gradient analysis, this paper reveals how mixup provides explicit, additional directional guidance when optimizing the normal distribution through AML, with its mixing coefficients acting as dynamic weights for this guidance. Based on these insights, we propose a self-supervised ASD method named AML-Driven and Distance-Aware Multi-Sample Mixing (ADMM). ADMM employs a novel distance-aware learning strategy that ensures focused optimization towards the target distribution while enriching the diversity of directional guidance, particularly for harder (e.g., boundary) samples. Comprehensive experiments on real-world datasets demonstrate the effectiveness and superior performance of the proposed ADMM method, achieving new state-of-the-art results within the mixup-AML framework.