ConUAD: Combating Noisy Data through Selective Contrastive Learning for Unsupervised Acoustic Anomaly Detection

Acoustic Anomaly Detection (AAD) has gained significant attention as a method for identifying faults or malicious activities. Previous state-of-the-art (SOTA) unsupervised AAD algorithms, particularly contrastive learning-based approaches, have advanced significantly beyond traditional models. However, their performance often deteriorates in real-world applications due to reliance on clean, noise-free training data. To address the challenge of noisy data, this paper proposes ConUAD, a selective contrastive learning framework for unsupervised AAD. The core idea of ConUAD is to mitigate the influence of noisy data by generating pseudo-labels to identify and select trustworthy pairs, thereby improving the robustness of representation learning within the contrastive learning framework. Experimental results on the real-world industrial MIMII dataset demonstrate the effectiveness of ConUAD, achieving a 3.22% improvement in AUC compared to previous state-of-the-art unsupervised methods.