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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Acknowledgements
This work was partly supported by FAPESP (2013/14185-2, 2015/23424-6), CNPq and ERC project 2012-ERC-AdG-320684 CHESS.
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Fantinato, D.G., Duarte, L.T., Rivet, B., Ehsandoust, B., Attux, R., Jutten, C. (2017). Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_29
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DOI: https://doi.org/10.1007/978-3-319-53547-0_29
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