SCAN: Selective Contrastive Learning Against Noisy Data for Acoustic Anomaly Detection

Abstract

Acoustic Anomaly Detection (AAD) has gained significant attention for the detection of suspicious activities or faults. Contrastive learning-based unsupervised AAD has outperformed traditional models on academic datasets, however, its model training is predominantly based on datasets containing only normal samples. In real industrial settings, a dataset of normal samples can still be corrupted by abnormal samples. Handling such noisy data is a crucial challenge, yet it remains largely unsolved. To address this issue, this letter proposes a Selective Contrastive learning framework Against Noisy data (SCAN) to mitigate the adverse effects of training the AAD model with anomaly-corrupted data. Specifically, SCAN progressively constructs confidence sample pairs based on the Mahalanobis distance, which is derived from the geometric median. These selected pairs are then integrated into the contrastive learning framework to enhance representation learning and model robustness. Extensive experiments under varying levels of label noise (i.e., the proportion of mislabeled abnormal samples in training data) demonstrate that SCAN outperforms state-of-the-art (SOTA) AAD methods on the real-world industrial datasets DCASE2022 and DCASE2024 Task2.