Channel sparsity aware polynomial expansion filters for nonlinear acoustic echo cancellation

Description

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<p>Speech quality is a demand in voice commanded systems and in telephony. The
voice communication system in real time often suffers from audible echoes. In order to cancel
echoes, an acoustic echo cancellation system is designed and applied to increase speech quality
both subjectively and objectively.
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<p>In this research we develop various nonlinear adaptive filters wielding the new channel
sparsity-aware recursive least squares (RLS) algorithms using a sequential update. The
developed nonlinear adaptive filters using the sparse sequential RLS (S-SEQ-RLS) algorithm
apply a discard function to disregard the coefficients which are not significant or close to zero in
the weight vector for each channel in order to reduce the computational load and improve the
algorithm convergence rate. The channel sparsity-aware algorithm is first derived for nonlinear
system modeling or system identification, and then modified for application of echo
cancellation. Simulation results demonstrate that by selecting a proper threshold value in the
discard function, the proposed nonlinear adaptive filters using the RLS (S-SEQ-RLS) algorithm
can achieve the similar performance as the nonlinear filters using the sequential RLS (SEQ-RLS)
algorithm in which the channel weight vectors are sequentially updated. Furthermore, the
proposed channel sparsity-aware RLS algorithms require a lower computational load in
comparison with the non-sequential and non-sparsity algorithms. The computational load for the
sparse algorithms can further be reduced by using data-selective strategies.
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Links & Downloads

1. 10.25394/pgs.7464584.v1

Tags

Nonlinear acoustic echo cancellation

system identification

3rd order volterra filter

functional link adaptive filter

even mirror fourier nonlinear filter

sparse RLS algorithm

sparse LMS algorithm

Additional Fields

Identifer	oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7464584
Date	16 January 2019
Creators	Vinith Vijayarajan (5930993)
Source Sets	Purdue University
Detected Language	English
Type	Text, Thesis
Rights	CC BY 4.0
Relation	https://figshare.com/articles/Channel_sparsity_aware_polynomial_expansion_filters_for_nonlinear_acoustic_echo_cancellation/7464584