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Comparative performance study between the Time-varying LMS (TVLMS) algorithm, LMS algorithm and RLS algorithm

Author(s): Kapil Belpatre | Bachute .M.R

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: ncipet;
Issue: 1;
Date: 2012;
Original page

Keywords: TV-LMS | LMS | NLMS

This paper presents a comparative performance study between the recently proposed time-varying LMS (TVLMS) algorithm and other two main adaptive approaches: the least-mean square (LMS) algorithm and the recursive least squares (RLS) algorithm. Implementational aspects of these algorithms and their computational complexity are examined. Using computer simulations, the successive trade-off between the computational complexity and system noise cancellation ability, as one proceeds from the Wiener estimate to the LMS with fixed step size, becomes apparent. Three performance criteria are utilized in this study: the algorithm execution time, the minimum mean squared error (MSE), and the required filter order. The study showed that the selection of the filter order is based on a trade-off between the MSE performance and algorithm executive time. Results also showed that the execution time of the RLS algorithm increases more rapidly with the filter order than other algorithms. Recently adaptive filtering was presented, have a nice tradeoff between complexity and the convergence speed. This paper also compares a new approach for noise cancellation in speech enhancement using the two new adaptive filtering algorithms named fast affine projection algorithm and fast Euclidean direction search algorithms for attenuating noise in speech signals. The simulation results demonstrate the good performance of the two new algorithms in attenuating the noise.

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