We propose an acoustic anomaly detection algorithm based on the
framework of contrastive learning. Contrastive learning is a recently
proposed self-supervised approach that has shown promising results in
image classification and speech recognition. However, its application in
anomaly detection is underexplored. Earlier studies have demonstrated
that it can achieve state-of-the-art performance in image anomaly
detection, but its capability in anomalous sound detection is yet to be
investigated. For the first time, we propose a contrastive
learning-based framework that is suitable for acoustic anomaly
detection. Since most existing contrastive learning approaches are
targeted toward images, the effect of other data transformations on the
performance of the algorithm is unknown. Our framework learns a
representation from unlabeled data by applying audio-specific data
augmentations. We show that in the resulting latent space, normal and
abnormal points are distinguishable. Experiments conducted on the MIMII
dataset confirm that our approach can outperform competing methods in
detecting anomalies.