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中文摘要：
介绍噪声抵消的原理和从强背景噪声中自适应滤波提取有用信号的方法,并对基于Sigmoid函数的变步长LMS算法（SVSLMS）、基于箕舌线的变步长LMS算法和基于抽样函数的变步长LMS算法进行对比研究,并将这三种改进型变步长算法用于强背景噪声中语音信号的提取,使其能消除含噪语音信号中的背景噪声,达到提高语音信号质量的目的。计算机模拟仿真结果表明,这三种算法都能通过有效抑制各种干扰来提高强噪声背景中的语音信号的检测特性。相比之下,基于抽样函数的变步长算法具有良好的收敛性能,更小的权噪声,更大的抑噪能力。
英文摘要：
The theory of noise canceling and the method for abstracting the desired signal from strong background noise were described by using adaptive filtering. The SVSLMS algorithms, the LMS algorithms based on tongue-like curve and the LMS algorithms based on Sa functions were compared. Three methods are used in speech signal extraction in stronger background noise. The results of computer simulation show that all of these adaptive algorithms can improve the detection of weak signal in strong background noise. But the LMS algorithms performance based on Sa functions algorithm is much better than the SVSLMS algorithms and the LMS algorithms based on tongue-like curve, having lower misadjustment noise, better robustness against noise and disturbance.
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