A Note on Robust Adaptive Volterra Filtering Based on Parallel Subgradient Projection Techniques

Isao YAMADA; Takuya OKADA; Kohichi SAKANIWA

A Note on Robust Adaptive Volterra Filtering Based on Parallel Subgradient Projection Techniques

Isao YAMADA, Takuya OKADA, Kohichi SAKANIWA

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A robust adaptive filtering algorithm was established recently (I. Yamada, K. Slavakis, K. Yamada 2002) based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. In this letter, we show the potential applicability of the adaptive algorithm to the identification problem for the second order Volterra systems. The numerical examples demonstrate that a straightforward application of the algorithm to the problem soundly realizes fast and stable convergence for highly colored excited speech like input signals in possibly noisy environments.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.8 pp.2065-2068

Publication Date: 2003/08/01

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Type of Manuscript: Special Section LETTER (Special Section on Digital Signal Processing)

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Isao YAMADA, Takuya OKADA, Kohichi SAKANIWA, "A Note on Robust Adaptive Volterra Filtering Based on Parallel Subgradient Projection Techniques" in IEICE TRANSACTIONS on Fundamentals, vol. E86-A, no. 8, pp. 2065-2068, August 2003, doi: .
Abstract: A robust adaptive filtering algorithm was established recently (I. Yamada, K. Slavakis, K. Yamada 2002) based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. In this letter, we show the potential applicability of the adaptive algorithm to the identification problem for the second order Volterra systems. The numerical examples demonstrate that a straightforward application of the algorithm to the problem soundly realizes fast and stable convergence for highly colored excited speech like input signals in possibly noisy environments.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e86-a_8_2065/_p

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@ARTICLE{e86-a_8_2065,
author={Isao YAMADA, Takuya OKADA, Kohichi SAKANIWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Note on Robust Adaptive Volterra Filtering Based on Parallel Subgradient Projection Techniques},
year={2003},
volume={E86-A},
number={8},
pages={2065-2068},
abstract={A robust adaptive filtering algorithm was established recently (I. Yamada, K. Slavakis, K. Yamada 2002) based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. In this letter, we show the potential applicability of the adaptive algorithm to the identification problem for the second order Volterra systems. The numerical examples demonstrate that a straightforward application of the algorithm to the problem soundly realizes fast and stable convergence for highly colored excited speech like input signals in possibly noisy environments.},
keywords={},
doi={},
ISSN={},
month={August},}

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TY - JOUR
TI - A Note on Robust Adaptive Volterra Filtering Based on Parallel Subgradient Projection Techniques
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2065
EP - 2068
AU - Isao YAMADA
AU - Takuya OKADA
AU - Kohichi SAKANIWA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2003
AB - A robust adaptive filtering algorithm was established recently (I. Yamada, K. Slavakis, K. Yamada 2002) based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. In this letter, we show the potential applicability of the adaptive algorithm to the identification problem for the second order Volterra systems. The numerical examples demonstrate that a straightforward application of the algorithm to the problem soundly realizes fast and stable convergence for highly colored excited speech like input signals in possibly noisy environments.
ER -