  We have developed an unsupervised anomalous sound detection method for
machine condition monitoring that utilizes an auxiliary task -- detecting when
the target machine is active. First, we train a model that detects machine
activity by using normal data with machine activity labels and then use the
activity-detection error as the anomaly score for a given sound clip if we have
access to the ground-truth activity labels in the inference phase. If these
labels are not available, the anomaly score is calculated through outlier
detection on the embedding vectors obtained by the activity-detection model.
Solving this auxiliary task enables the model to learn the difference between
the target machine sounds and similar background noise, which makes it possible
to identify small deviations in the target sounds. Experimental results showed
that the proposed method improves the anomaly-detection performance of the
conventional method complementarily by means of an ensemble.
