Abstract: In order to optimize the efficiency of combined features in abnormal sound recognition,an algorithm for detecting the effectiveness of abnormal sound frame signals and extracting multi-layer features using ensemble empirical mode decomposition (EEMD) is proposed. Firstly,an ensemble empirical mode decomposition is performed on the abnormal sound frame signal to obtain the intrinsic model function,and then the validity of the frame signal is tested according to the given layer threshold of the intrinsic modal function. Finally,the Mel frequency cepstral coefficients,the inverted Mel frequency cepstral coefficients,the linear prediction cepstral coefficients,the short-time energy and energy ratio are extracted for each layer of the intrinsic modal function of the effective frame signal,and then all of them are normalized and spliced into multi-layer feature. According to the extracted features,the deep convolutional neural network is used to realize the classification and recognition of abnormal sound. Simulations show that the proposed new method can achieve a recognition rate of 98.65% in four types of abnormal sound recognition.

Key words: abnormal sound recognition, ensemble empirical mode decomposition, multi-layer feature, deep convolutional neural network