CLF-AIAD: A Contrastive Learning Framework for Acoustic Industrial Anomaly Detection

Acoustic Industrial Anomaly Detection (AIAD) has received a great deal of attention as a technique to discover faults or malicious activity, allowing for preventive measures to be more effectively targeted. The essence of AIAD is to learn the compact distribution of normal acoustic data and detect outliers as anomalies during testing. However, recent AIAD work does not capture the dependencies and dynamics of Acoustic Industrial Data (AID). To address this issue, we propose a novel Contrastive Learning Framework (CLF) for AIAD, known as CLF-AIAD. Our method introduces a multi-grained contrastive learning-based framework to extract robust normal AID representations. Specifically, we first employ a projection layer and a novel context-based contrast method to learn robust temporal vectors. Building upon this, we then introduce a sample-wise contrasting-based module to capture local invariant characteristics, improving the discriminative capabilities of the model. Finally, a transformation classifier is introduced to bolster the performance of the primary task under a self-supervised learning framework. Extensive experiments on two typical industrial datasets, MIMII and ToyADMOS, demonstrate that our proposed CLF-AIAD effectively detects various real-world defects and improves upon the state-of-the-art in unsupervised industrial anomaly detection.