An Efficient Adaptive Filtering Scheme Based on Combining Multiple Metrics
Summary :
We propose a novel adaptive filtering scheme named metric-combining normalized least mean square (MC-NLMS). The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metric-matrices convexly in an adaptive manner, thereby taking advantages of the metrics which rely on multiple pieces of information. We compare the improved PNLMS (IPNLMS) algorithm with the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, and it is shown that the performance of NPNLMS is controllable with the combination coefficient as opposed to IPNLMS. We also present an application to an acoustic echo cancellation problem and show the efficacy of the proposed scheme.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.3 pp.800-808
- Publication Date
- 2014/03/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E97.A.800
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Osamu TODA
Keio University
Masahiro YUKAWA
Keio University
Shigenobu SASAKI
Niigata University
Hisakazu KIKUCHI
Niigata University
Keyword
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Osamu TODA, Masahiro YUKAWA, Shigenobu SASAKI, Hisakazu KIKUCHI, "An Efficient Adaptive Filtering Scheme Based on Combining Multiple Metrics" in IEICE TRANSACTIONS on Fundamentals,
vol. E97-A, no. 3, pp. 800-808, March 2014, doi: 10.1587/transfun.E97.A.800.
Abstract: We propose a novel adaptive filtering scheme named metric-combining normalized least mean square (MC-NLMS). The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metric-matrices convexly in an adaptive manner, thereby taking advantages of the metrics which rely on multiple pieces of information. We compare the improved PNLMS (IPNLMS) algorithm with the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, and it is shown that the performance of NPNLMS is controllable with the combination coefficient as opposed to IPNLMS. We also present an application to an acoustic echo cancellation problem and show the efficacy of the proposed scheme.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.800/_p
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@ARTICLE{e97-a_3_800,
author={Osamu TODA, Masahiro YUKAWA, Shigenobu SASAKI, Hisakazu KIKUCHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Efficient Adaptive Filtering Scheme Based on Combining Multiple Metrics},
year={2014},
volume={E97-A},
number={3},
pages={800-808},
abstract={We propose a novel adaptive filtering scheme named metric-combining normalized least mean square (MC-NLMS). The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metric-matrices convexly in an adaptive manner, thereby taking advantages of the metrics which rely on multiple pieces of information. We compare the improved PNLMS (IPNLMS) algorithm with the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, and it is shown that the performance of NPNLMS is controllable with the combination coefficient as opposed to IPNLMS. We also present an application to an acoustic echo cancellation problem and show the efficacy of the proposed scheme.},
keywords={},
doi={10.1587/transfun.E97.A.800},
ISSN={1745-1337},
month={March},}
Copy
TY - JOUR
TI - An Efficient Adaptive Filtering Scheme Based on Combining Multiple Metrics
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 800
EP - 808
AU - Osamu TODA
AU - Masahiro YUKAWA
AU - Shigenobu SASAKI
AU - Hisakazu KIKUCHI
PY - 2014
DO - 10.1587/transfun.E97.A.800
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2014
AB - We propose a novel adaptive filtering scheme named metric-combining normalized least mean square (MC-NLMS). The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metric-matrices convexly in an adaptive manner, thereby taking advantages of the metrics which rely on multiple pieces of information. We compare the improved PNLMS (IPNLMS) algorithm with the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, and it is shown that the performance of NPNLMS is controllable with the combination coefficient as opposed to IPNLMS. We also present an application to an acoustic echo cancellation problem and show the efficacy of the proposed scheme.
ER -
English
