文章基本信息

标题：Filtering Non-Stationary Noise in Speech Signals using Computationally Efficient Unbiased and Normalized Algorithm
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作者：Md Zia Ur Rahman ; Sk. Khaja Mohedden ; M. Ajay Kumar 等
期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2011
卷号：3
期号：3
页码：1106-1113
出版社：Engg Journals Publications
摘要：Extraction of high resolution information signals is important in all practical applications. The Least Mean Square (LMS) algorithm is a basic adaptive algorithm has been extensively used in many applications as a consequence of its simplicity and robustness. In this paper we present a novel adaptive filter for de-noising the speech signals based on unbiased and normalized adaptive noise reduction (UNANR) algorithm. The UNANR model does not contain a bias unit, and the coefficients are adaptively updated by using the steepest-descent algorithm. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy speech, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with speech in the primary input. To measure the ability of the proposed implementation, signal to noise ratio improvement (SNRI) is calculated. The results show that the performance of the UNANR based algorithm is superior to that of the LMS and conventional Normalized LMS (NLMS) algorithms in noise reduction
关键词：Adaptive filtering; LMS algorithm; MSE; Noise cancellation; Speech enhancement.