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

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 tha...

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Bibliographic Details
Published inInternational journal of modeling and optimization Vol. 2; no. 3; pp. 351 - 355
Main Authors Santosh, D Hari Hara, Pendyala, VUSL Sravya, Kumar, V N Lakshman, Rao, N Shanmukh
Format Journal Article
LanguageEnglish
Published 01.06.2012
Subjects
Adaptive filters
Blocking
Central processing units
Convergence
Mathematical models
Noise
Optimization
Speech
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Summary: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.
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ISSN:2010-3697
2010-3697
DOI:10.7763/IJMO.2012.V2.142