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

Anomalous Sound Detection Based on Machine Activity Detection