摘要
针对宽带噪声背景下的语音增强问题,将短时语音视为非平稳或宽平稳信号,基于谱减法和自适应滤波的最小均方(LMS)算法,提出了一种FIR型自适应滤波算法(SSLMS):用减谱法由短时噪声观测语音估计期望信号,作为滤波器输出信号的参考信号;用滤波器的输出与参考信号的差值为误差信号,用LMS算法求得滤波器权系数修正量,并修正滤波器。权系数最速下降调整中,采用了归一化LMS、符号LMS、块LMS技术,以简化保证权系数收敛的步长选择、减少权系数修正的运算量,从而提高自适应速度。对不同的语音在各种信噪比下仿真实验,并与改进的谱减法比较,结果表明,该法增强效果优于谱减法;在信噪比为3dB时该法的增强效果仍然令人满意。
FIR adaptive filtering algorithm can improve the adaptive speed based on spectral subtraction and LMS of the short-term non-stationary or wide range signals for speech enhancement in condition broadband noise. This algorithm detects an expected speech signal of derived from short-term noise with spectral subtraction, this expected signal is used as reference signal of the output filter. The method improves performance of filter and chooses differences as the error signal between the referenced signal and the output filter. The filters of reconstruction are revised directly by filter corrective value based on LMS of weigh. This algorithm adopts the way of normalization LMS and symbolization LMS and blocking LMS in the steepest descent adjusting the weights, and simplifies step selection of converge weight, and reduces the computation of corrective weight. The simulation experiment shows that the algorithm can effectively enhance speech of all kinds of SNR, and it is superior to spectral subtraction. The result shows that the enhancement method is satisfaction in the 3 dB.
出处
《计算机工程与应用》
CSCD
2012年第7期142-145,共4页
Computer Engineering and Applications
关键词
自适应滤波
语音增强
LMS算法
权系数
最速下降法
adaptive filtering
speech enhancement
LMS algorithm
weights
steepest descent method
