一种优化的VMD算法及其在语音信号去噪中的应用

针对非连续、 非平稳语音信号中含有噪声的问题, 提出一种基于参数优化的变分模态分解去噪算法. 首先, 利用灰狼优化算法搜寻变分模态分解算法的最优分解参数组合——分解模态数K和惩罚因子α, 通过使用获得的参数组合分解语音信号以获得K个特征模态函数分量IMF; 其次, 利用相关系数选择有效模态分量, 并用小波阈值处理无效模态分量； 最后, 重构小波阈值处理后的模态分量和有效模态分量以对语音信号进行去噪. 实验结果表明, 该算法与其他经典算法相比能有效提升信噪比, 降低均方误差, 提高语音信号的质量.

An Optimized VMD Algorithm and Its Application in Speech Signal Denoising

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.