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To develop a machine sound monitoring system, a method for detectinganomalous sound is proposed. Exact likelihood estimation using NormalizingFlows is a promising technique for unsupervised anomaly detection, but it canfail at out-of-distribution detection since the likelihood is affected by thesmoothness of the data. To improve the detection performance, we train themodel to assign higher likelihood to target machine sounds and lower likelihoodto sounds from other machines of the same machine type. We demonstrate thatthis enables the model to incorporate a self-supervised classification-basedapproach. Experiments conducted using the DCASE 2020 Challenge Task2 datasetshowed that the proposed method improves the AUC by 4.6% on average when usingMasked Autoregressive Flow (MAF) and by 5.8% when using Glow, which is asignificant improvement over the previous method.

Flow-based Self-supervised Density Estimation for Anomalous Sound Detection