Neural Network Models for Blind Separation of Time Delayed and Convolved Signals
Summary :
In this paper we develop a new family of on-line adaptive learning algorithms for blind separation of time delayed and convolved sources. The algorithms are derived for feedforward and fully connected feedback (recurrent) neural networks on basis of modified natural gradient approach. The proposed algorithms can be considered as generalization and extension of existing algorithms for instantaneous mixture of unknown source signals. Preliminary computer simulations confirm validity and high performance of the proposed algorithms.
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- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.9 pp.1595-1603
- Publication Date
- 1997/09/25
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- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
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Andrzej CICHOCKI, Shun-ichi AMARI, Jianting CAO, "Neural Network Models for Blind Separation of Time Delayed and Convolved Signals" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 9, pp. 1595-1603, September 1997, doi: .
Abstract: In this paper we develop a new family of on-line adaptive learning algorithms for blind separation of time delayed and convolved sources. The algorithms are derived for feedforward and fully connected feedback (recurrent) neural networks on basis of modified natural gradient approach. The proposed algorithms can be considered as generalization and extension of existing algorithms for instantaneous mixture of unknown source signals. Preliminary computer simulations confirm validity and high performance of the proposed algorithms.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_9_1595/_p
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@ARTICLE{e80-a_9_1595,
author={Andrzej CICHOCKI, Shun-ichi AMARI, Jianting CAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Neural Network Models for Blind Separation of Time Delayed and Convolved Signals},
year={1997},
volume={E80-A},
number={9},
pages={1595-1603},
abstract={In this paper we develop a new family of on-line adaptive learning algorithms for blind separation of time delayed and convolved sources. The algorithms are derived for feedforward and fully connected feedback (recurrent) neural networks on basis of modified natural gradient approach. The proposed algorithms can be considered as generalization and extension of existing algorithms for instantaneous mixture of unknown source signals. Preliminary computer simulations confirm validity and high performance of the proposed algorithms.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Neural Network Models for Blind Separation of Time Delayed and Convolved Signals
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1595
EP - 1603
AU - Andrzej CICHOCKI
AU - Shun-ichi AMARI
AU - Jianting CAO
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1997
AB - In this paper we develop a new family of on-line adaptive learning algorithms for blind separation of time delayed and convolved sources. The algorithms are derived for feedforward and fully connected feedback (recurrent) neural networks on basis of modified natural gradient approach. The proposed algorithms can be considered as generalization and extension of existing algorithms for instantaneous mixture of unknown source signals. Preliminary computer simulations confirm validity and high performance of the proposed algorithms.
ER -
English
