A Joint Second-Order Statistics and Density Matching-Based Approach for Separation of Post-Nonlinear Mixtures

Denis G Fantinato (1) , Leonardo T Duarte (1) , Paolo Zanini (2, 3) , Bertrand Rivet (3) , Romis Attux (1) , Christian Jutten (3)

1 DSPCom - Laboratory of Signal Processing for Communications
2 IMS - Laboratoire de l'intégration, du matériau au système
3 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

In the context of Post-Nonlinear (PNL) mixtures, source separation can be performed in a two-stage approach, which encompasses a nonlinear and a linear compensation part. In the former part, however , it is usually required the knowledge of the distribution type for all sources, what may be difficult to attend. In view of this, in this work, we propose a less restrictive approach, in which it is required the knowledge of a single source distribution – here, chosen to be a colored Gaussian. The other sources are only required to present a time structure. The method combines, in a joint-based approach, the use of the second-order statistics (SOS) and the matching of distributions, which shows to be less costly than the classical method of computing the marginal entropy for all sources. The simulation results are favorable to the proposal.

Mots clés: Blind Source Separation; Density Matching; Post-Nonlinear Mixtures; Second- Order Statistics

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

Denis G Fantinato, Leonardo T Duarte, Paolo Zanini, Bertrand Rivet, Romis Attux, et al.. A Joint Second-Order Statistics and Density Matching-Based Approach for Separation of Post-Nonlinear Mixtures. LVA/ICA 2017 - 13th International Conference on Latent Variable Analysis and Signal Separation, Olivier Michel; Nadège Thirion-Moreau, Feb 2017, Grenoble, France. pp.499 - 508, ⟨10.1007/978-3-319-53547-0_47⟩. ⟨hal-01479431⟩