Separation of Sparse Signals in Overdetermined Linear-Quadratic Mixtures

Leonardo Tomazeli Duarte (1) , Rafael Assato Ando (2) , Romis Attux (1) , Yannick Deville (3) , Christian Jutten (4)

1 DSPCom - Digital Signal Processing and Communications laboratory [Campinas]
2 UNICAMP - Universidade Estadual de Campinas = University of Campinas
3 IRAP - Institut de recherche en astrophysique et planétologie
4 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

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.

Mots clés: Nonlinear mixtures; sparse signals; blind source separation

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Leonardo Tomazeli Duarte, Rafael Assato Ando, Romis Attux, Yannick Deville, Christian Jutten. Separation of Sparse Signals in Overdetermined Linear-Quadratic Mixtures. LVA/ICA 2012 - 10th International Conference on Latent Variable Analysis and Signal Separation, Mar 2012, Tel-Aviv, Israel. pp.239-246. ⟨hal-00687408⟩