Titre :	Blind source separation
Type de document :	texte imprimé
Auteurs :	Qasem Bendermel, Auteur ; Mohamed Arezki Adel Belouchrani, Directeur de thèse ; M. Adnane, Directeur de thèse
Editeur :	[S.l.] : [s.n.]
Année de publication :	2017
Importance :	40 f.
Présentation :	ill.
Format :	30 cm.
Accompagnement :	1 CD-ROM.
Note générale :	Mémoire de Master : Electronique : Alger, Ecole Nationale Polytechnique : 2017 Bibliogr. f. 39 - 40
Langues :	Anglais (eng)
Mots-clés :	Blind source separation Independent Component Analysis Signal process-ing
Index. décimale :	Ms13317
Résumé :	Blind Source Separation (BSS) is a statistical approach to separating individual signals from an observed mixture of a group of signals. BSS relies on only very weak assumptions on the signals and the mixing process and this blindness enables the technique to be used in a wide variety of situations. Research in the field of Blind Source Separation has resulted in the development of a family of algorithms, known as Independent Component Analysis (ICA) algorithms, that can reliably and efficiently achieve blind separation of signals. There are two important problems that are generally considered: instantaneous BSS and convolutive BSS. The difference between these two is based on the nature of the signal mixing process. In this thesis, the mathematical foundations of both instanta-neous and convolutive BSS are developed. Once this mathematical framework has been established, the emphasis of the thesis moves to experimental results obtained with ICA techniques .

Blind source separation [texte imprimé] / Qasem Bendermel, Auteur ; Mohamed Arezki Adel Belouchrani, Directeur de thèse ; M. Adnane, Directeur de thèse . - [S.l.] : [s.n.], 2017 . - 40 f. : ill. ; 30 cm. + 1 CD-ROM.
Mémoire de Master : Electronique : Alger, Ecole Nationale Polytechnique : 2017
Bibliogr. f. 39 - 40
Langues : Anglais (eng)

Mots-clés :	Blind source separation Independent Component Analysis Signal process-ing
Index. décimale :	Ms13317
Résumé :	Blind Source Separation (BSS) is a statistical approach to separating individual signals from an observed mixture of a group of signals. BSS relies on only very weak assumptions on the signals and the mixing process and this blindness enables the technique to be used in a wide variety of situations. Research in the field of Blind Source Separation has resulted in the development of a family of algorithms, known as Independent Component Analysis (ICA) algorithms, that can reliably and efficiently achieve blind separation of signals. There are two important problems that are generally considered: instantaneous BSS and convolutive BSS. The difference between these two is based on the nature of the signal mixing process. In this thesis, the mathematical foundations of both instanta-neous and convolutive BSS are developed. Once this mathematical framework has been established, the emphasis of the thesis moves to experimental results obtained with ICA techniques .