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EURASIP Journal on Advances in Signal Processing
Article . 2016 . Peer-reviewed
License: CC BY
Data sources: Crossref
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The generalized frequency-domain adaptive filtering algorithm as an approximation of the block recursive least-squares algorithm

Authors: Schneider, Martin; Kellermann, Walter;
APC: 1,047.2 EUR

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

Abstract

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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Keywords

DDC Classification::6 Technik, Medizin, angewandte Wissenschaften :: 62 Ingenieurwissenschaften :: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten, Acoustic echo cancellation, System identification, Generalized frequency-domain adaptive filtering

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Top 10%
Top 10%
gold