Adaptive Combination of Proportionate Filters for Sparse Echo Cancellation
Jerónimo Arenas-Garcia and Aníbal Figueiras-Vidal
IEEE Trans. Audio, Speech and Language Processing
Proportionate adaptive filters, such as those based on 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 regarding their convergence properties and steady-state error.
In this paper, we study how combination schemes, where the outputs
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. We also introduce a new block-based combination scheme which is
specifically designed to further exploit the characteristics of the IPNLMS filter.
The advantages of these combined filters are justified theoretically and illustrated in several
echo cancellation scenarios.