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    Abstract
2012 (Vol. 4, Issue: 17)
Article Information:

Noise Minimization from Speech Signals using RLS Algorithm with Variable Forgetting Factor

V.K. Gupta, Mahesh Chandra and S.N. Sharan
Corresponding Author:  V.K. Gupta 

Key words:  Adaptive filter, DSRLS, mean square error (MSE), signal to noise ratio (SNR), SRLS, VFFRLS,
Vol. 4 , (17): 3102-3107
Submitted Accepted Published
March 10, 2012 March 30, 2012 September 01, 2012
Abstract:

In this study RLS algorithm with Double Log-Sigmoid function (DSRLS) is proposed to minimize the effect of noise from speech signals. The performance of DSRLS is compared with the performance of RLS and RLS algorithm with Log-Sigmoid function (SRLS). Experiments were performed on noisy data which was prepared by adding machine gun, F16 and speech noise to clean speech samples at -5dB, 0dB, 5dB and 10dB SNR levels. The simulation results show that both SRLS and DSRLS perform better than RLS in terms of SNR improvement. However, DSRLS performs best in terms of SNR improvement with MSE decrement.
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  Cite this Reference:
V.K. Gupta, Mahesh Chandra and S.N. Sharan, 2012. Noise Minimization from Speech Signals using RLS Algorithm with Variable Forgetting Factor.  Research Journal of Applied Sciences, Engineering and Technology, 4(17): 3102-3107.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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