An Adaptive Echo Canceller with Variable Step Gain Method

Seiichi YAMAMOTO; Seishi KITAYAMA

An Adaptive Echo Canceller with Variable Step Gain Method

Seiichi YAMAMOTO, Seishi KITAYAMA

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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 double-talking" without any control device such as a double-talking detector.

Publication: IEICE TRANSACTIONS on transactions Vol.E65-E No.1 pp.1-8

Publication Date: 1982/01/25

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Type of Manuscript: PAPER

Category: Transmission Systems

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Seiichi YAMAMOTO
Seishi KITAYAMA

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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 double-talking" without any control device such as a double-talking detector.
URL: https://global.ieice.org/en_transactions/transactions/10.1587/e65-e_1_1/_p

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@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 double-talking" without any control device such as a double-talking detector.},
keywords={},
doi={},
ISSN={},
month={January},}

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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 double-talking" without any control device such as a double-talking detector.
ER -