IEICE TRANSACTIONS on transactions
An Adaptive Echo Canceller with Variable Step Gain Method
Summary :
As a means of improving the rate of convergence of the conventional echo canceller using the learning identification method, the authors have previously proposed a linear predictive algorithm. This algorithm shows better convergence than the learning identification method. However, in this algorithm, as well as in the learning identification method, a compromise is necessary between a relatively large step gain required for fast convergence and the relatively small step gain needed for noise insensitivity in the presence of noise. In this paper a new algorithm based on the linear predicitive algorithm is proposed, in which the step gain is determined as a function of the estimated values of noise and the parameters-error of the echo path model in order to improve both the rate of convergence and the noise insensitivity simultaneously. The efficiency of the proposed algorithm is examined by computer simulations. It has been shown that the proposed algorithm gives about twice the rate of convergence and about 10 dB lower parameters-error in the stationary state in comparison with the learning identification method. Besides, it has been proved that this algorithm guarantees non-divergence of the echo path model even during the period of
- Publication
- IEICE TRANSACTIONS on transactions Vol.E65-E No.1 pp.1-8
- Publication Date
- 1982/01/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Transmission Systems
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Seiichi YAMAMOTO, Seishi KITAYAMA, "An Adaptive Echo Canceller with Variable Step Gain Method" in IEICE TRANSACTIONS on transactions,
vol. E65-E, no. 1, pp. 1-8, January 1982, doi: .
Abstract: As a means of improving the rate of convergence of the conventional echo canceller using the learning identification method, the authors have previously proposed a linear predictive algorithm. This algorithm shows better convergence than the learning identification method. However, in this algorithm, as well as in the learning identification method, a compromise is necessary between a relatively large step gain required for fast convergence and the relatively small step gain needed for noise insensitivity in the presence of noise. In this paper a new algorithm based on the linear predicitive algorithm is proposed, in which the step gain is determined as a function of the estimated values of noise and the parameters-error of the echo path model in order to improve both the rate of convergence and the noise insensitivity simultaneously. The efficiency of the proposed algorithm is examined by computer simulations. It has been shown that the proposed algorithm gives about twice the rate of convergence and about 10 dB lower parameters-error in the stationary state in comparison with the learning identification method. Besides, it has been proved that this algorithm guarantees non-divergence of the echo path model even during the period of
URL: https://global.ieice.org/en_transactions/transactions/10.1587/e65-e_1_1/_p
Copy
@ARTICLE{e65-e_1_1,
author={Seiichi YAMAMOTO, Seishi KITAYAMA, },
journal={IEICE TRANSACTIONS on transactions},
title={An Adaptive Echo Canceller with Variable Step Gain Method},
year={1982},
volume={E65-E},
number={1},
pages={1-8},
abstract={As a means of improving the rate of convergence of the conventional echo canceller using the learning identification method, the authors have previously proposed a linear predictive algorithm. This algorithm shows better convergence than the learning identification method. However, in this algorithm, as well as in the learning identification method, a compromise is necessary between a relatively large step gain required for fast convergence and the relatively small step gain needed for noise insensitivity in the presence of noise. In this paper a new algorithm based on the linear predicitive algorithm is proposed, in which the step gain is determined as a function of the estimated values of noise and the parameters-error of the echo path model in order to improve both the rate of convergence and the noise insensitivity simultaneously. The efficiency of the proposed algorithm is examined by computer simulations. It has been shown that the proposed algorithm gives about twice the rate of convergence and about 10 dB lower parameters-error in the stationary state in comparison with the learning identification method. Besides, it has been proved that this algorithm guarantees non-divergence of the echo path model even during the period of
keywords={},
doi={},
ISSN={},
month={January},}
Copy
TY - JOUR
TI - An Adaptive Echo Canceller with Variable Step Gain Method
T2 - IEICE TRANSACTIONS on transactions
SP - 1
EP - 8
AU - Seiichi YAMAMOTO
AU - Seishi KITAYAMA
PY - 1982
DO -
JO - IEICE TRANSACTIONS on transactions
SN -
VL - E65-E
IS - 1
JA - IEICE TRANSACTIONS on transactions
Y1 - January 1982
AB - As a means of improving the rate of convergence of the conventional echo canceller using the learning identification method, the authors have previously proposed a linear predictive algorithm. This algorithm shows better convergence than the learning identification method. However, in this algorithm, as well as in the learning identification method, a compromise is necessary between a relatively large step gain required for fast convergence and the relatively small step gain needed for noise insensitivity in the presence of noise. In this paper a new algorithm based on the linear predicitive algorithm is proposed, in which the step gain is determined as a function of the estimated values of noise and the parameters-error of the echo path model in order to improve both the rate of convergence and the noise insensitivity simultaneously. The efficiency of the proposed algorithm is examined by computer simulations. It has been shown that the proposed algorithm gives about twice the rate of convergence and about 10 dB lower parameters-error in the stationary state in comparison with the learning identification method. Besides, it has been proved that this algorithm guarantees non-divergence of the echo path model even during the period of
ER -
English
