语音信号中各种自适应噪声消除算法的比较研究

Aniket Kumar, Pankaj Goel, V. Gupta, M. Chandra
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引用次数: 6

摘要

在现实生活中,信号和噪声的统计特性通常是未知的,因此具有“常系数”的数字滤波器几乎没有任何用处。在这种情况下,自适应滤波器是可取的。自适应滤波器能够根据输入信号和噪声特性的异常调整滤波器系数,从而获得无噪声的信号。本文讨论了LMS (Least mean square)、BLMS (Block LMS)、NLMS (Normalized LMS)、BNLMS (Block NLMS)、VSLMS (Variable step size LMS)和BVSLMS (Block VSLMS)算法等各种自适应滤波算法的比较分析。作为输入,我们使用印地语音频语音信号和Babble噪声作为干扰信号。
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Comparative research of various adaptive algorithms for noise cancellation in speech signals
In real life situations, the statistical characteristics of signal and noise are generally unknown & hence a digital filter having ‘constant coefficients’ is hardly of any use. In such situations adaptive filter is desirable. Adaptive filters are capable of adapting their filter coefficients as per the abnormality in characteristics of input signal and noise to achieve a noise free signal. This paper discusses the comparative analysis of various adaptive filter algorithms such as LMS (Least mean square), BLMS (Block LMS), NLMS (Normalized LMS), BNLMS (Block NLMS), VSLMS (Variable step size LMS) and BVSLMS (Block VSLMS) algorithms. As a input we have used Hindi audio speech signal and Babble noise as an interference signal.
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