Statistical Approach for Noise Removal in Speech Signals Using LMS, NLMS, Block LMS and RLS Adaptive filters - Volume 2 Number 3 (Jun. 2012) - ijmo
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General Information
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2012 Vol.2(3): 351-355 ISSN: 2010-3697
DOI: 10.7763/IJMO.2012.V2.142

Statistical Approach for Noise Removal in Speech Signals Using LMS, NLMS, Block LMS and RLS Adaptive filters

D. Hari Hara Santosh, VUSL Sravya Pendyala, V. N. Lakshman Kumar, and N. Shanmukh Rao

Abstract—In this paper the application of different adaptive filters in removing the noise present in the speech signals is presented. To analyze the performance of different adaptive filter family members, the parameters like convergence, output PSNR and CPU consumption time are considered. Results show that NLMS filter shows the better performance in CPU time consumption and output PSNR. Block LMS has the highest Convergence factor among all the members of the adaptive filter family.

Index Terms—Adaptive filters, LMS, RLS, NLMS, NLMS

The authors are with department of ECE. (e-mail:8ant08h-ece2005@yahoo.com).

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Cite: Hari Hara Santosh, VUSL Sravya Pendyala, V.N. Lakshman Kumar, and N. Shanmukh Rao, "Statistical Approach for Noise Removal in Speech Signals Using LMS, NLMS, Block LMS and RLS Adaptive filters," International Journal of Modeling and Optimization vol. 2, no. 3, pp. 351-355, 2012.

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