| Full text | |
| Author(s): |
Nose-Filho, K.
;
Duarte, L. T.
;
Romano, J. M. T.
;
Tichavsky, P
;
BabaieZadeh, M
;
Michel, OJJ
;
ThirionMoreau, N
Total Authors: 7
|
| Document type: | Journal article |
| Source: | LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2017); v. 10169, p. 10-pg., 2017-01-01. |
| Abstract | |
Disjoint Component Analysis (DCA) is a recent blind source separation approach which is based on the assumption that the original sources have disjoint supports. In DCA, the recovery process is carried out by maximizing the disjoint support of the estimated sources. In the present work, we provide sufficient conditions for the separation of both disjoint and quasi-disjoint signals. In addition, we propose an effective DCA criterion to evaluate the level of superposition of the recovered sources. The minimization of such criterion is implemented by an algorithm based on Givens rotations. Finally, simulation results are presented in order to assess the performance of the proposed method. (AU) | |
| FAPESP's process: | 15/07048-4 - Blind Source Separation: Sparse Component Analysis for Convolutive Mixtures and Nonlinear Mixtures |
| Grantee: | Kenji Nose Filho |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |