An Effective Anomalous Sound Detection Method Based on Representation Learning with Simulated Anomalies

In this paper, we propose an effective anomalous sound detection (ASD) method based on representation learning with simulated anomalies. Recently, ASD systems have used Outlier Exposure (OE) strategy to achieve promising performance in DCASE challenges. These exploit deep Convolutional Neural Networks (CNNs) to learn discriminative representations by treating the normal samples from different classes as pseudo anomalies. However, since the anomalous sounds occur only rarely, are diversely distributed and are unseen during training, the OE capability of representations learned from normal samples may be limited. To address this issue, we propose a statistics exchange (StEx) method, which constructs simulated anomalies to improve the effectiveness of representation learning via OE strategy. Specifically, the first and second order statistics are extracted from time or frequency axis of input spectrograms, and the simulated anomaly is then obtained by exchanging the statistics of spectrograms from different classes. Furthermore, an out-of-distribution (OOD) metric is introduced as an importance measure to qualitatively analyze OE capability, which enables appropriate simulated anomaly selection for ASD. Extensive experiments on the DCASE2021 challenge task2 development dataset verify the effectiveness of representation learning with simulated anomaly for OE based ASD.