Separation of sparse signals in overdetermined linear-quadratic mixtures

Authors:
Leonardo T. Duarte;Rafael A. Ando;Romis Attux;Yannick Deville;Christian Jutten
Affiliations:
School of Applied Sciences, University of Campinas (UNICAMP), Campinas, Brazil;School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, Brazil;School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, Brazil;IRAP, Université de Toulouse, CNRS, Toulouse, France;GIPSA-Lab, CNRS UMR-5216, Grenoble, France
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

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Abstract

In this work, we deal with the problem of nonlinear blind source separation (BSS). We propose a new method for BSS in overdetermined linear-quadratic (LQ) mixtures. By exploiting the assumption that the sources are sparse in a transformed domain, we define a framework for canceling the nonlinear part of the mixing process. After that, separation can be conducted by linear BSS algorithms. Experiments with synthetic data are performed to assess the viability of our proposal.