Using Taylor Series Expansions and Second-Order Statistics for Blind Source Separation in Post-Nonlinear Mixtures

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Author(s): Show less -	Fantinato, Denis G. ; Duarte, Leonardo T. ; Deville, Yannick ; Jutten, Christian ; Attux, Romis ; Neves, Aline ; Deville, Y ; Gannot, S ; Mason, R ; Plumbley, MD ; Ward, D Total Authors: 11
Document type:	Journal article
Source:	LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2018); v. 10891, p. 11-pg., 2018-01-01.
Abstract
In the context of Post-Nonlinear (PNL) mixtures, source separation based on Second-Order Statistics (SOS) is a challenging topic due to the inherent difficulties when dealing with nonlinear transformations. Under the assumption that sources are temporally colored, the existing SOS-inspired methods require the use of Higher-Order Statistics (HOS) as a complement in certain stages of PNL demixing. However, a recent study has shown that the sole use of SOS is sufficient for separation if certain constraints on the separation system are obeyed. In this paper, we propose the use of a PNL separating model based on constrained Taylor series expansions which is able to satisfy the requirements that allow a successful SOS-based source separation. The simulation results corroborate the proposal effectiveness. (AU)

FAPESP's process:	17/11488-5 - Multivariate Analysis of the Data Temporal Structure for Blind Source Separation in the Context of Nonlinear Mixtures and of Multiple Datasets
Grantee:	Denis Gustavo Fantinato
Support Opportunities:	Scholarships in Brazil - Post-Doctoral