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A Low Delay and Fast Converging Improved Proportionate Algorithm for Sparse System Identification

EURASIP Journal on Audio, Speech, and Music Processing volume 2007, Article number: 084376 (2007) Cite this article

Abstract

A sparse system identification algorithm for network echo cancellation is presented. This new approach exploits both the fast convergence of the improved proportionate normalized least mean square (IPNLMS) algorithm and the efficient implementation of the multidelay adaptive filtering (MDF) algorithm inheriting the beneficial properties of both. The proposed IPMDF algorithm is evaluated using impulse responses with various degrees of sparseness. Simulation results are also presented for both speech and white Gaussian noise input sequences. It has been shown that the IPMDF algorithm outperforms the MDF and IPNLMS algorithms for both sparse and dispersive echo path impulse responses. Computational complexity of the proposed algorithm is also discussed.

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Affiliations

  1. Department of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, London, SW7 2AZ, UK

    Andy W.H. Khong & Patrick A. Naylor

  2. INRS-EMT, Université du Québec, Suite 6900, 800 de la Gauchetière Ouest, Montréal, QC, H5A 1K6, Canada

    Jacob Benesty

Authors
  1. Andy W.H. Khong
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  2. Patrick A. Naylor
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  3. Jacob Benesty
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Corresponding author

Correspondence to Andy W.H. Khong.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Khong, A.W., Naylor, P.A. & Benesty, J. A Low Delay and Fast Converging Improved Proportionate Algorithm for Sparse System Identification. J AUDIO SPEECH MUSIC PROC. 2007, 084376 (2007). https://doi.org/10.1155/2007/84376

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  • DOI: https://doi.org/10.1155/2007/84376

Keywords

  • Impulse Response
  • Acoustics
  • White Gaussian Noise
  • Fast Convergence
  • Input Sequence