Location of Repository

JOINT BLIND SOURCE SEPARATION FROM SECOND-ORDER STATISTICS: NECESSARY AND SUFFICIENT IDENTIFIABILITY CONDITIONS

By Javier Vía, Matthew Anderson, Xi-lin Li and Tülay Adalı

Abstract

This paper considers the problem of joint blind source separation (J-BSS), which appears in many practical problems such as blind deconvolution or functional magnetic resonance imaging (fMRI). In particular, we establish the necessary and sufficient conditions for the solution of the J-BSS problem by exclusively exploiting the second-order statistics (SOS) of the observations. The identifiability analysis is based on the idea of equivalently distributed sets of latent variables, that is, latent variables with covariance matrices related by means of a diagonal matrix. Interestingly, the identifiability analysis also allows us to introduce a measure of the identifiability degree based on Kullback-Leibler projections. This measure is clearly correlated with the performance of practical SOS-based J-BSS algorithms, which is illustrated by means of numerical examples. Index Terms — Joint blind source separation (J-BSS), independent vector analysis (IVA), second-order statistics, identifiability. 1

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.309.6659
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.csee.umbc.edu/~adal... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.