A Riemannian approach to blind separation of t-distributed sources - Université Grenoble Alpes Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

A Riemannian approach to blind separation of t-distributed sources

Résumé

The blind source separation problem is considered, more specifically the approach based on non-stationarity and coloration. In both cases, sources are usually assumed to be Gaus-sian. In this paper, we extend previous works in order to handle sources drawn from the multivariate Student t-distribution. After studying the data model in this case, a new blind source separation criterion based on the log-likelihood of the considered distribution is proposed. To solve the resulting optimization problem, Riemannian optimization on the parameter manifold is leveraged. The performance of the proposed method is illustrated on simulated data.
Fichier principal
Vignette du fichier
EUSIPCO2020_robustBSS_a.pdf (279.16 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02988356 , version 1 (04-11-2020)

Identifiants

Citer

Florent Bouchard, Arnaud Breloy, Guillaume Ginolhac, Alexandre Renaux. A Riemannian approach to blind separation of t-distributed sources. 2020 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam, Netherlands. ⟨10.23919/eusipco47968.2020.9287783⟩. ⟨hal-02988356⟩
47 Consultations
19 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More