ABNORMAL SOUND DETECTING DEVICE, ABNORMALITY MODEL LEARNING DEVICE, ABNORMALITY DETECTING DEVICE, ABNORMAL SOUND DETECTING METHOD, ABNORMAL SOUND GENERATING DEVICE, ABNORMAL DATA GENERATING DEVICE, ABNORMAL SOUND GENERATING METHOD, AND PROGRAM

摘要

Accuracy of unsupervised anomalous sound detection is improved using a small number of pieces of anomalous sound data. A threshold deciding part (13) calculates an anomaly score for each of a plurality of pieces of anomalous sound data, using a normal model learned with normal sound data and an anomaly model expressing the pieces of anomalous sound data, and decides a minimum value among the anomaly scores as a threshold. A weight updating part (14) updates, using a plurality of pieces of normal sound data, the pieces of anomalous sound data and the threshold, weights of the anomaly model so that all the pieces of anomalous sound data are judged as anomalous, and probability of the pieces of normal sound data being judged as anomalous is minimized.