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Nonlinear Blind Source Separation for Statistically Dependent Sources

Grant number: 15/23424-6
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): February 15, 2016
Effective date (End): July 14, 2016
Field of knowledge:Engineering - Electrical Engineering - Telecommunications
Principal researcher:Romis Ribeiro de Faissol Attux
Grantee:Denis Gustavo Fantinato
Supervisor abroad: Christian Jutten
Home Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Research place: Université de Grenoble, France  
Associated to the scholarship:13/14185-2 - New Methods for Adaptive Equalization Based on Information Theoretic Learning, BP.DR


The nonlinear blind source separation (BSS) problem is a challenging research topic and, differently from the linear case, its development has not reached a general separation framework. Among the reported nonlinear models, we highlight two instances that are crucial for the present project: (i) post-nonlinear (PNL) mixtures and (ii) contexts of separation of colored sources using signal derivatives. In the former case, the assumption of mutual independence among sources can be suitable for solving the PNL problem, but the employment of criteria solely based on independence constrains the nonlinearities to be bijective. Hence, there emerges as an interesting possibility the use of other statistics, such as spatial and/or temporal dependencies, for the adoption of a larger set of nonlinear separation functions. For the second approach, if temporally colored sources are assumed, by computing derivatives of the mixtures, the nonlinear mixing process becomes linear and instantaneous, but time-varying. In that sense, by modeling the probability density function of the derivatives of the sources, a more robust criterion based on the matching of distributions can be applied. The project is intended to be developed at GIPSA-Lab, Grenoble, France, under the supervision of Prof. Christian Jutten, a pioneer in the linear and nonlinear BSS problems.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
FANTINATO, DENIS G.; DUARTE, LEONARDO T.; DEVILLE, YANNICK; ATTUX, ROMIS; JUTTEN, CHRISTIAN; NEVES, ALINE. A second-order statistics method for blind source separation in post-nonlinear mixtures. Signal Processing, v. 155, p. 63-72, FEB 2019. Web of Science Citations: 1.

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