Anomalous sound detection apparatus, degree-of-anomaly calculation apparatus, anomalous sound generation apparatus, anomalous sound detection training apparatus, anomalous signal detection apparatus, anomalous signal detection training apparatus, and methods and programs therefor

Abstract

To provide an anomalous sound detection training technique by which a feature amount extraction function for detecting anomalous sound can be generated irrespective of whether training data for anomalous signals is available or not. An anomalous sound detection training apparatus includes: a first function updating unitthat updates a feature amount extraction function and an feature amount inverse transformation function, which are input, based on an optimization index of a variational autoencoder; an acoustic feature extraction unitthat extracts an acoustic feature of normal sound based on training data for normal sound; a normal sound model updating unitthat updates a normal sound model by using the acoustic feature that is extracted; a threshold updating unitthat obtains a threshold φcorresponding to a false positive rate ρ, which has a predetermined value, by using the training data for normal sound and the feature amount extraction function that is input; and a second function updating unitthat updates the feature amount extraction function that is updated, based on a Neyman-Pearson-type optimization index defined by the threshold φthat is obtained, and repeatedly performs processing of each of the above-mentioned units.