  Acoustic monitoring for machine fault detection is a recent and expanding
research path that has already provided promising results for industries.
However, it is impossible to collect enough data to learn all types of faults
from a machine. Thus, new algorithms, trained using data from healthy
conditions only, were developed to perform unsupervised anomaly detection. A
key issue in the development of these algorithms is the noise in the signals,
as it impacts the anomaly detection performance. In this work, we propose a
powerful data-driven and quasi non-parametric denoising strategy for spectral
data based on a tensor decomposition: the Non-negative Canonical Polyadic (CP)
decomposition. This method is particularly adapted for machine emitting
stationary sound. We demonstrate in a case study, the Malfunctioning Industrial
Machine Investigation and Inspection (MIMII) baseline, how the use of our
denoising strategy leads to a sensible improvement of the unsupervised anomaly
detection. Such approaches are capable to make sound-based monitoring of
industrial processes more reliable.
