Improving Convergence of the PNLMS Algorithm in Sparse Echo Cancellation using SVS-PNLMS Algorithm

Improving Convergence of the PNLMS Algorithm in Sparse Echo Cancellation using SVS-PNLMS Algorithm

Abstract

In this paper SVS-PNLMS algorithm is proposed. The analysis reveals that it performs a faster convergence rate compared to that of the recently introduced SPNLMS, PNLMS algorithms. Compared with its proportionate counterparts e.g. PNLMS and SPNLMS, the proposed SVS-PNLMS algorithm not only results in a faster convergence rate for both white and colored noise inputs, but also preserves its initial fast convergence rate until it reaches to its steady state condition. It also presents a higher tracking behavior for quasi-stationary inputs such as speech signal in addition to better performance in terms of computational complexity and resulting ERLE. In addition, the proposed SVS-PNLMS algorithm is also evaluated with previously proposed algorithms in a theoretical framework which validates the computer simulation results in terms of CPU time and number of iterations needed for each algorithm to get converged. Finally, a region of convergence for the proposed algorithm is derived for different input cases including white, colored noise and speech signal. This region is also compared with the practical value usually used in echo cancellation application.

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