Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures

Abstract : 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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Petr Tichavsky; Massoud Babaie-Zadeh; Olivier Michel; Nadège Thirion-Moreau. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Feb 2017, Grenoble, France. Springer, Lecture Notes in Computer Science, 10169, pp.300 - 309, 2017, Latent Variable Analysis and Signal Separation 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings. 〈10.1007/978-3-319-53547-0_29〉
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Denis Fantinato, Leonardo Duarte, Bertrand Rivet, Bahram Ehsandoust, Romis Attux, et al.. Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures. Petr Tichavsky; Massoud Babaie-Zadeh; Olivier Michel; Nadège Thirion-Moreau. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Feb 2017, Grenoble, France. Springer, Lecture Notes in Computer Science, 10169, pp.300 - 309, 2017, Latent Variable Analysis and Signal Separation 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings. 〈10.1007/978-3-319-53547-0_29〉. 〈hal-01480992〉

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