Self-Supervised Learning with Attention-Guided Statistics Exchange for Anomalous Sound Detection

Self-supervised Anomalous Sound Detection (ASD) relies on proxy anomaly generation to train discriminative models without real anomalous samples. However, existing generation strategies largely rely on manual design and operate without principled guidance, producing proxy anomalies of unguaranteed quality. Statistical exchange partially mitigates this by preserving spectrogram structure through distribution-level perturbation, but still depends on preset dimensions and indiscriminate perturbation. This paper proposes Attention-Guided Statistics Exchange (AG-StatEx) to establish traceable guidance for principled proxy anomaly generation. To establish this guidance, four lightweight attention-based modules are designed to jointly evaluate the importance of time-frequency feature dimensions and sub-components, enabling adaptive selection of the signal-aggregated dimension and the most discriminative sub-components for targeted anomalization. Furthermore, a shrinkage mechanism progressively narrows the anomalization range during training, implicitly regularizing the model to focus on the most stable and discriminative signal structures. We also develop an attention heatmap-based visualization method to improve interpretability and propose a new metric to assess proxy anomaly quality. Extensive experiments on DCASE2022-2025 and a constructed ASD dataset demonstrate the superior effectiveness, generalization capability, and interpretability of AG-StatEx.