Multi-Resolution Acoustic Prototype Anchoring for Limited-Sample Industrial Anomalous Sound Detection

Abstract

Industrial anomalous sound detection is important for machine condition monitoring, especially when anomalous recordings are unavailable and only a small number of normal recordings can be collected from the target machine. This paper proposes target-domain cosine prototype anchoring (TD-CPA), a lightweight and interpretable framework for limited-sample industrial anomalous sound detection. The method combines multi-resolution Log-Mel representations with global, temporal-delta, and segment-wise statistical features to characterize both stable operating patterns and short-duration acoustic variations. PCA is then used to obtain compact embeddings, while ten target-normal recordings are employed to construct a target acoustic prototype. Anomaly scores are calculated based on the cosine deviation between each test embedding and this prototype. Experiments covering seven industrial machine categories show that TD-CPA achieves mean AUC, pAUC, and HScore values of 0.6067, 0.1389, and 0.2095, respectively, representing the highest numerical mean performance among the evaluated statistical, distance-based, compact autoencoder, and frozen pretrained-embedding baselines. Ablation experiments confirm the complementary contributions of the statistical feature components and multiple time-frequency resolutions. The target-sample sensitivity experiment further demonstrates that broader target-normal coverage generally improves detection performance. Overall, TD-CPA provides a simple, normal-data-only, and interpretable solution for industrial anomalous sound detection under limited target-data conditions.