IEICE TRANSACTIONS on Fundamentals
Alternative Learning Algorithm for Stereophonic Acoustic Echo Canceller without Pre-Processing
Summary :
This paper proposes an alternative learning algorithm for a stereophonic acoustic echo canceller without pre-processing which can identify the correct echo-paths. By dividing the filter coefficients into the former/latter parts and updating them alternatively, conditions both for unique solution and for perfect echo cancellation are satisfied. The learning for each part is switched from one part to the other when that part converges. Convergence analysis clarifies the condition for correct echo-path identification. For fast and stable convergence, a convergence detection and an adaptive step-size are introduced. The modification amount of the filter coefficients determines the convergence state and the step-size. Computer simulations show 10 dB smaller filter coefficient error than those of the conventional algorithms without pre-processing.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.8 pp.1958-1964
- Publication Date
- 2004/08/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Digital Signal Processing)
- Category
- Speech/Acoustic Signal Processing
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Akihiro HIRANO, Kenji NAKAYAMA, Daisuke SOMEDA, Masahiko TANAKA, "Alternative Learning Algorithm for Stereophonic Acoustic Echo Canceller without Pre-Processing" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 8, pp. 1958-1964, August 2004, doi: .
Abstract: This paper proposes an alternative learning algorithm for a stereophonic acoustic echo canceller without pre-processing which can identify the correct echo-paths. By dividing the filter coefficients into the former/latter parts and updating them alternatively, conditions both for unique solution and for perfect echo cancellation are satisfied. The learning for each part is switched from one part to the other when that part converges. Convergence analysis clarifies the condition for correct echo-path identification. For fast and stable convergence, a convergence detection and an adaptive step-size are introduced. The modification amount of the filter coefficients determines the convergence state and the step-size. Computer simulations show 10 dB smaller filter coefficient error than those of the conventional algorithms without pre-processing.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_8_1958/_p
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@ARTICLE{e87-a_8_1958,
author={Akihiro HIRANO, Kenji NAKAYAMA, Daisuke SOMEDA, Masahiko TANAKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Alternative Learning Algorithm for Stereophonic Acoustic Echo Canceller without Pre-Processing},
year={2004},
volume={E87-A},
number={8},
pages={1958-1964},
abstract={This paper proposes an alternative learning algorithm for a stereophonic acoustic echo canceller without pre-processing which can identify the correct echo-paths. By dividing the filter coefficients into the former/latter parts and updating them alternatively, conditions both for unique solution and for perfect echo cancellation are satisfied. The learning for each part is switched from one part to the other when that part converges. Convergence analysis clarifies the condition for correct echo-path identification. For fast and stable convergence, a convergence detection and an adaptive step-size are introduced. The modification amount of the filter coefficients determines the convergence state and the step-size. Computer simulations show 10 dB smaller filter coefficient error than those of the conventional algorithms without pre-processing.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Alternative Learning Algorithm for Stereophonic Acoustic Echo Canceller without Pre-Processing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1958
EP - 1964
AU - Akihiro HIRANO
AU - Kenji NAKAYAMA
AU - Daisuke SOMEDA
AU - Masahiko TANAKA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2004
AB - This paper proposes an alternative learning algorithm for a stereophonic acoustic echo canceller without pre-processing which can identify the correct echo-paths. By dividing the filter coefficients into the former/latter parts and updating them alternatively, conditions both for unique solution and for perfect echo cancellation are satisfied. The learning for each part is switched from one part to the other when that part converges. Convergence analysis clarifies the condition for correct echo-path identification. For fast and stable convergence, a convergence detection and an adaptive step-size are introduced. The modification amount of the filter coefficients determines the convergence state and the step-size. Computer simulations show 10 dB smaller filter coefficient error than those of the conventional algorithms without pre-processing.
ER -
English
