Filtering Non-Stationary Noise in Speech Signals using Computationally Efficient Unbiased and Normalized Algorithm
Journal Title: International Journal on Computer Science and Engineering - Year 2011, Vol 3, Issue 3
Abstract
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
Authors and Affiliations
Md Zia Ur Rahman , Sk. Khaja Mohedden , Dr. B V Rama Mohana Rao , Y. Jaipal Reddy2 , G. V. S. Karthik
Keywords
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