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. | ISSN:2249-5789 Mohammad Zia-Ur-Rahman et al, International Journal of Computer Science & Communication Networks,Vol 1(1),September-October 2011 Battle Field Speech Enhancement using an Efficient Unbiased Adaptive Filtering Technique Sk. Khaja Mohedden, Mohammad Zia-Ur-Rahman, K. Murali Krishna and Dr B V Rama Mohana Rao Dept. of ., Narasaraopeta Engg. College, Narasaraopeta-522 601, India E-mail: mdzr_5@ 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 Keywords — Adaptive filtering, LMS algorithm, MSE, Noise cancellation, Speech enhancement. . I. INTRODUCTION In real time environment speech signals are corrupted by several forms of noise such as competing speakers, background noise, car noise, and also they are subjected to distortion caused by communication channels; examples are room reverberation, low-quality microphones, etc. In all such situations extraction of high .
