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New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation
Author(s) -
Djendi Mohamed
Publication year - 2015
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2470
Subject(s) - algorithm , stereophonic sound , echo (communications protocol) , computer science , transversal (combinatorics) , convergence (economics) , filter (signal processing) , adaptive filter , computation , kalman filter , trace (psycholinguistics) , scheme (mathematics) , field (mathematics) , type (biology) , mathematics , channel (broadcasting) , artificial intelligence , telecommunications , computer network , mathematical analysis , computer vision , economic growth , linguistics , philosophy , ecology , pure mathematics , biology , economics
Summary This paper addresses the field of stereophonic acoustic echo cancelation (SAEC) by adaptive filtering algorithms. Recently, simplified versions of the fast transversal filter (SFTF)‐type algorithm has been proposed. In this paper, we propose two major contributions. In the first contribution, we propose two new FTF‐type algorithms with low complexity and good convergence speed characteristics. These two proposed algorithms are mainly on the basis of a forward prediction scheme to estimate the so called dual Kalman gain, which is inherent in the filtering part update. This computation complexity is achieved by the introduction of new relations for the computation of the likelihood variables that are simple and lead to further simplifications on the prediction part of the two proposed algorithms. In the second contribution, we propose to adapt then apply these four new SFTF‐type algorithms, (the two proposed algorithms in this paper and their original versions) in the SAEC applications. A fair comparison of the proposed algorithms with the original SFTF and the normalized least mean square algorithms, in mono and SAEC applications, is presented. Copyright © 2014 John Wiley & Sons, Ltd.