  To develop a machine sound monitoring system, a method for detecting
anomalous sound is proposed. Exact likelihood estimation using Normalizing
Flows is a promising technique for unsupervised anomaly detection, but it can
fail at out-of-distribution detection since the likelihood is affected by the
smoothness of the data. To improve the detection performance, we train the
model to assign higher likelihood to target machine sounds and lower likelihood
to sounds from other machines of the same machine type. We demonstrate that
this enables the model to incorporate a self-supervised classification-based
approach. Experiments conducted using the DCASE 2020 Challenge Task2 dataset
showed that the proposed method improves the AUC by 4.6% on average when using
Masked Autoregressive Flow (MAF) and by 5.8% when using Glow, which is a
significant improvement over the previous method.
