一种基于清浊音分离的动态阈值小波去噪方法

低信噪比下，传统的小波去噪算法会造成语音信号中有用信息的损失，从而导致去噪性能的下降。针对这一问题，提出了一种基于清浊音分离的动态阈值小波去噪方法。采用谱减法去除部分噪声，再运用短时能量法判别清浊音，有效地降低了误判率；融入了小波包分解法以保护清音部分不被损失；根据各层的分解系数来动态地确定阈值，以避免过平滑真实信号；采用了一种新的阈值函数，有效弥补了软、硬阈值函数在去噪性能上的不足。仿真结果表明，该方法能较好地提高语音信号的重构质量。

Wavelet de-noising method by dynamic threshold based on separation of voiced signal and unvoiced signal

In low SNR, thc traditional wavelet de-noising method may lose some useful ingredients of speech signal,resulting in low de-noising performance.As to the problem,this paper recommends a wavelet de-noising method by dynamic threshold based on separation of voiced signal and unvoiced signal.This method reduces a part of noise by spectral subtraction, and separates voiced signal from unvoiced signal with shorttime energy.Wavelet package is used to protect the unvoiced signal.Avoiding to smooth speech signal, the method decides the dynamic threshold by the coefficients from each layer.A new threshold function is used to make up the disadvantages of soft-threshold and hard-threshold.By simulation testing, this method can improve the reconstructed quality of speech signal.