  To develop a sound-monitoring system for machines, a method for detecting
anomalous sound under domain shifts is proposed. A domain shift occurs when a
machine's physical parameters change. Because a domain shift changes the
distribution of normal sound data, conventional unsupervised anomaly detection
methods can output false positives. To solve this problem, the proposed method
constrains some latent variables of a normalizing flows (NF) model to represent
physical parameters, which enables disentanglement of the factors of domain
shifts and learning of a latent space that is invariant with respect to these
domain shifts. Anomaly scores calculated from this domain-shift-invariant
latent space are unaffected by such shifts, which reduces false positives and
improves the detection performance. Experiments were conducted with sound data
from a slide rail under different operation velocities. The results show that
the proposed method disentangled the velocity to obtain a latent space that was
invariant with respect to domain shifts, which improved the AUC by 13.2% for
Glow with a single block and 2.6% for Glow with multiple blocks.
