PerMix: Anomalous sound detection by predicting the perturbation ratio of mixed samples

In anomalous sound detection based on self-supervised learning, the performance of the detection model largely depends on the selection of the pretext task through which the characteristics of normal data are learned. A common pretext task is to mix samples from different classes and classify the mixed sample as its target class with an angular margin loss. However, this supervision becomes mismatched when the target sample is the minority component of the mixture and the margin remains fixed irrespective of the mixing ratio. To address these issues, we propose a pretext task that directly predicts the perturbation ratio of mixed samples by reformulating the logit so that the angular margin is scaled by the perturbation ratio. In addition, we adopt a supervised contrastive loss modified for mixed samples, in which positive pairs are formed according to the dominant class. Experiments show that our approach achieves state-of-the-art results on the DCASE 2020 Task 2 benchmark. Our code is available at https://github.com/kaistss/PerMix.