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Communication Dans Un Congrès Année : 2017

Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures

Résumé

In this work, we consider the nonlinear Blind Source Separation (BSS) problem in the context of overdetermined Bilinear Mixtures, in which a linear structure can be employed for performing separation. Based on the Gaussian Process (GP) framework, two approaches are proposed: the predictive distribution and the maximization of the marginal likelihood. In both cases, separation can be achieved by assuming that the sources are Gaussian and temporally correlated. The results with synthetic data are favorable to the proposal.
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Dates et versions

hal-01480992 , version 1 (02-03-2017)

Identifiants

Citer

Denis G Fantinato, Leonardo T Duarte, Bertrand Rivet, Bahram Ehsandoust, Romis Attux, et al.. Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures. LVA/ICA 2017 - 13th International Conference on Latent Variable Analysis and Signal Separation, Olivier Michel; Nadège Thirion-Moreau, Feb 2017, Grenoble, France. pp.300 - 309, ⟨10.1007/978-3-319-53547-0_29⟩. ⟨hal-01480992⟩
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