Abstract: Aiming at the problem that normal and abnormal sounds might have great similarity and sometimes could not distinguish normal and abnormal sounds by the size of reconstruction error of autoencoder, we proposed a generative adversarial single classification network method for abnormal sound detection. Through multiple training, the method learned the distribution characteristics of normal samples. In the test process, the normal sample could be reconstructed with minimal error, while the abnormal sample had poor reconstruction effect, and distortion occurred in some frequency bands, so as to give the discriminant classification results. In the experiment, UrbanSound8K public data set and measured motor sound data set were used for testing, and the accuracy of this method is 86.3% and 98.1%, respectively, which is 5.0% and 3.0% higher than main deep learning methods such as convolutional autoencoder.

Key words:  , autoencoder, generative adversarial network, sound anomaly detection