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小波分析具有时频局部化和多分辨率特性,而小波包分析是在小波分析的基础上对信号高频部分的更精细地分解,选取合适的小波基一直是小波包去噪分析中的关键问题。将熵函数作为选取最优小波基的评价标准,通过计算语音信号小波包分解系数的熵值来确定合适的分解方式,同时采用小波包阈值去噪算法对三种小波基进行小波包去噪仿真实验,并进行对比分析。仿真实验表明,两种熵函数选取的最优小波基都能较好地消除强噪声背景下的噪声,得到信噪比较高的语音信号。
Wavelet analysis has time-frequency localization and multi-resolution characteristics, and wavelet packet analysis is based on the wavelet analysis of the high-frequency part of the signal more detailed decomposition, select the appropriate wavelet base has always been the key wavelet packet denoising analysis problem. Taking the entropy function as the evaluation criterion for the selection of the optimal wavelet base, an appropriate decomposition method is determined by calculating the entropy of the wavelet packet decomposition coefficients of the speech signal. At the same time, the wavelet packet threshold denoising algorithm is used to perform wavelet packet denoising simulation Experiments, and comparative analysis. The simulation results show that the optimal wavelet bases selected by the two entropy functions can eliminate the noise in the strong noise environment and obtain the speech signal with high SNR.