关键词: 语音信号, 灰狼算法, 变分模态分解, 小波阈值, 相关系数

Abstract: Aiming at the problem of noise in non-continuous and non-stationary speech signals, we proposed a variational mode decomposition (VMD) denoising algorithm based on parameter optimization. Firstly, the grey wolf optimization algorithm was used to search the optimal decomposition parameter combination of the VMD algorithm: decomposition mode number K and penalty factor α. By using the combination of the obtained parameter combination to decompose the speech signal, K characteristic mode function components IMF were obtained. Secondly, the effective modal components were selected by the correlation coefficient, and the invalid modal components were processed by the wavelet threshold. Finally, the wavelet threshold processed modal component and effective modal component were reconstructed to denoise the speech signal. Experimental results show that compared with other classical algorithms, the proposed algorithm can effectively improve the signal-to-noise ratio (SNR), reduce the mean square error, and improve the quality of speech signals.

Key words: speech signal, grey wolf algorithm, variational mode decomposition, wavelet threshold, correlation coefficient