The generalized frequency-domain adaptive filtering algorithm as an approximation of the block recursive least-squares algorithm

Acoustic echo cancellation (AEC) is a well-known application of adaptive filters in communication acoustics. To implement AEC for multichannel reproduction systems, powerful adaptation algorithms like the generalized frequency-domain adaptive filtering (GFDAF) algorithm are required for satisfactory...

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Vydáno v:EURASIP journal on advances in signal processing Ročník 2016; číslo 1; s. 1 - 15
Hlavní autoři: Schneider, Martin, Kellermann, Walter
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 15.01.2016
Springer
Springer Nature B.V
Témata:
Adaptive filters
Algorithms
Analysis
Approximation
Blocking
Engineering
Least squares method
Mathematical analysis
Mathematical models
Multichannel
Quantum Information Technology
Signal,Image and Speech Processing
Spintronics
Germany
Generalized frequency-domain adaptive filtering
Acoustic echo cancellation
System identification
ISSN:1687-6180, 1687-6172, 1687-6180
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Shrnutí:Acoustic echo cancellation (AEC) is a well-known application of adaptive filters in communication acoustics. To implement AEC for multichannel reproduction systems, powerful adaptation algorithms like the generalized frequency-domain adaptive filtering (GFDAF) algorithm are required for satisfactory convergence behavior. In this paper, the GFDAF algorithm is rigorously derived as an approximation of the block recursive least-squares (RLS) algorithm. Thereby, the original formulation of the GFDAF algorithm is generalized while avoiding an error that has been in the original derivation. The presented algorithm formulation is applied to pruned transform-domain loudspeaker-enclosure-microphone models in a mathematically consistent manner. Such pruned models have recently been proposed to cope with the tremendous computational demands of massive multichannel AEC. Beyond its generalization, a regularization of the GFDAF is shown to have a close relation to the well-known block least-mean-squares algorithm.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-015-0302-2