Anomalous Sound Detection Based on Convolutional Neural Network and Mixed Features

Abstract

In this paper, convolutional neural network (CNN) is used to detect abnormal sound. Aiming at the problem that the audio time-frequency graph contains complex feature information and the single traditional audio feature contains insufficient information, a new feature graph is proposed, which can be used as the input of the CNN model to get more accurate detection results. Firstly, the two-dimensional time-frequency graph is obtained by the short-time Fourier transform of the abnormal sound signal. Secondly, this paper extracts audio features such as MFCC and short-term energy from the time-frequency map and the original audio. Finally, this paper uses matrix transformation to combine the features and takes the processed feature graph as the input of the CNN model. And the CNN model was trained to judge the category of abnormal sound. The results of measured data show that the combination of CNN and mixed features can accurately judge and classify abnormal sounds.