PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Adaptive combination of IPNLMS filters for robust sparse echo cancellation
Jerónimo Arenas-Garcia and Aníbal R. Figueiras-Vidal
In: IEEE Machine Learning for Signal Processing (MLSP'08), Cancun(2008).

Abstract

Proportionate adaptive filters, such as the improved proportionate normalized least-mean-square (IPNLMS) algorithm, have been proposed for echo cancellation as an interesting alternative to the normalized least-mean-square (NLMS) filter. Proportionate schemes offer improved performance when the echo path is sparse, but are still subject to some compromises. In this paper, we study how combination schemes, where the output of two independent adaptive filters are adaptively mixed together, can be used to increase IPNLMS robustness to channels with different degrees of sparsity, as well as to alleviate the rate of convergence vs steady-state misadjustment tradeoff imposed by the selection of the step size. The advantages of these combined filters are illustrated in several echo cancellation scenarios.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Speech
Theory & Algorithms
ID Code:5022
Deposited By:Jerónimo Arenas-Garcia
Deposited On:24 March 2009