Krishna Samalla; Ch. Satyanarayana

Modified Sparseness Controlled IPNLMS Algorithm Based on l_1, l_2 and l_∞ Norms

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Author(s)

Krishna Samalla ^1,* Ch. Satyanarayana ¹

1. Department of Computer Science and Engineering Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2013.04.03

Received: 3 Jan. 2013 / Revised: 30 Jan. 2013 / Accepted: 1 Mar. 2013 / Published: 8 Apr. 2013

Index Terms

Acoustic Echo Cancellation (AEC), Modified Sparseness Controlled Improved Proportionate Normalized Least Mean Square (MSC-IPNLMS), Sparseness Controlled Improved Proportionate Normalized Lease Mean Square (SC-IPNLMS), Sparseness measure

Abstract

In the context of Acoustic Echo Cancellation (AEC), sparseness level of acoustic impulse response (AIR) varies greatly in mobile environments. The modified sparseness-controlled Improved PNLMS (MSC-IPNLMS) algorithm proposed in this paper, exploits the sparseness measure of AIR using l1, l2 and l∞ norms. The MSC-IPNLMS is the modified version of SC-IPNLMS which uses sparseness measure based on l1 and l2 norms. Sparseness measure using l1, l2 and l∞ norms is the good representation of both sparse and dense impulse response, where as the measure which uses l1 and l2 norms is the good representation of sparse impulse response only. The MSC-IPNLMS is based on IPNLMS which allocates adaptation step size gain in proportion to the magnitude of estimated filter weights. By estimating the sparseness of the AIR, the proposed MSC-IPNLMS algorithm assigns the gains for each step size such that the proportionate term of the IPNLMS will be given higher weighting for sparse impulse responses. For a less sparse impulse response, a higher weighting will be given to the NLMS term. Simulation results, with input as white Gaussian noise (WGN), show the improved performance over the SC-IPNLMS algorithm in both sparse and dense AIR.

Cite This Paper

Krishna Samalla,Ch.Satyanarayana,"Modified Sparseness Controlled IPNLMS Algorithm Based on l_1, l_2 and l_∞ Norms", IJIGSP, vol.5, no.4, pp.18-29, 2013. DOI: 10.5815/ijigsp.2013.04.03

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[27]Krishna Samalla,Dr.Ch Satyanarayana"Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation" I.J. Image, Graphics and Signal Processing, 2013, 1, 16-24 Published Online January 2013 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijigsp.2013.1.03

International Journal of Image, Graphics and Signal Processing (IJIGSP)