Ｒecognition of Instruments’Sounds Based on VMD and PSO - HUANG Ying-lai; ＲEN Tian-li; ZHAO Peng (Journal of Harbin University of Science and Technology)
Proposing the method that based on the variational mode decomposition ( VMD) and particle swarm optimization ( PSO) optimized support vector machine ( SVM) are used to recognize the audio signals of the musical instruments aiming at the problem of the low recognition rate of musical instruments audio signals． In this paper， firstly，the instrument audio signals are decomposed into a series of stable narrowband components ( IMF) by VMD． After decomposition，according to the correlation coefficient we reconstruct the signals，then using the wavelet to remove the residual noises． Finally，based on the analysis of the traditional sound features extraction method，extracting the Mel frequency cepstral coefficients ( MFCC) and then SVM whose parameters are optimized by PSO is used to recognize the audio signals． This expserimental results show that the denoising effect of the proposed algorithm in this paper is better than that of empirical mode decomposition ( EMD) and ensemble empirical mode decomposition ( EEMD) ; SVM optimized by PSO effectively improve the accuracy of audio signals’ classification in noisy environment．