The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article présente une nouvelle méthode adaptative de séparation aveugle des sources (BSS) pour les mélanges linéaires et non linéaires. Les sources sont supposées statistiquement indépendantes avec des PDF non uniformes et symétriques. L'algorithme est basé à la fois sur des méthodes de recuit simulé et d'estimation de la densité utilisant un réseau neuronal. En considérant les propriétés des espaces vectoriels des sources et des mélanges, et en utilisant une certaine linéarisation dans l'espace des mélanges, la nouvelle méthode est dérivée. Enfin, les principales caractéristiques de la méthode sont la simplicité et la convergence rapide validées expérimentalement par la séparation de nombreux types de signaux, tels que la parole ou les données biomédicales.
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Carlos G. PUNTONET, Ali MANSOUR, "Blind Separation of Sources Using Density Estimation and Simulated Annealing" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 10, pp. 2538-2546, October 2001, doi: .
Abstract: This paper presents a new adaptive blind separation of sources (BSS) method for linear and non-linear mixtures. The sources are assumed to be statistically independent with non-uniform and symmetrical PDF. The algorithm is based on both simulated annealing and density estimation methods using a neural network. Considering the properties of the vectorial spaces of sources and mixtures, and using some linearization in the mixture space, the new method is derived. Finally, the main characteristics of the method are simplicity and the fast convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_10_2538/_p
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@ARTICLE{e84-a_10_2538,
author={Carlos G. PUNTONET, Ali MANSOUR, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Blind Separation of Sources Using Density Estimation and Simulated Annealing},
year={2001},
volume={E84-A},
number={10},
pages={2538-2546},
abstract={This paper presents a new adaptive blind separation of sources (BSS) method for linear and non-linear mixtures. The sources are assumed to be statistically independent with non-uniform and symmetrical PDF. The algorithm is based on both simulated annealing and density estimation methods using a neural network. Considering the properties of the vectorial spaces of sources and mixtures, and using some linearization in the mixture space, the new method is derived. Finally, the main characteristics of the method are simplicity and the fast convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.},
keywords={},
doi={},
ISSN={},
month={October},}
Copier
TY - JOUR
TI - Blind Separation of Sources Using Density Estimation and Simulated Annealing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2538
EP - 2546
AU - Carlos G. PUNTONET
AU - Ali MANSOUR
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2001
AB - This paper presents a new adaptive blind separation of sources (BSS) method for linear and non-linear mixtures. The sources are assumed to be statistically independent with non-uniform and symmetrical PDF. The algorithm is based on both simulated annealing and density estimation methods using a neural network. Considering the properties of the vectorial spaces of sources and mixtures, and using some linearization in the mixture space, the new method is derived. Finally, the main characteristics of the method are simplicity and the fast convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.
ER -