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

In this paper, we propose a self-supervised density estimation To develop a machine sound monitoring system, a method for detecting method using NF. Our method uses sound data from one machine ID anomalous sound is proposed. Exact likelihood estimation to detect anomalies (target data) and sound data from other machines using Normalizing Flows is a promising technique for unsupervised of the same machine type (outlier data), and the model is trained to anomaly detection, but it can fail at out-of-distribution detection assign higher likelihood to the target data and lower likelihood to since the likelihood is affected by the smoothness of the data. To improve the outlier data. This method is a self-supervised approach because the detection performance, we train the model to assign higher it improves the detection performance on one machine ID by introducing likelihood to target machine sounds and lower likelihood to sounds an auxiliary task in which the model discriminates the sound from other machines of the same machine type. We demonstrate that data of that machine ID (target data) from sound data of other machine this enables the model to incorporate a self-supervised classificationbased IDs with the same machine type (outlier data).