4.7 Article

Joint Blind Source Separation With Multivariate Gaussian Model: Algorithms and Performance Analysis

期刊

IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 60, 期 4, 页码 1672-1683

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2011.2181836

关键词

Canonical correlation analysis; independent component analysis; independent vector analysis; joint blind source separation; permutation problem; second-order statistics

资金

  1. NSF [NSF-IIS 0612076, NSF-IIS 1017718]
  2. Direct For Computer & Info Scie & Enginr
  3. Division of Computing and Communication Foundations [1117056] Funding Source: National Science Foundation

向作者/读者索取更多资源

In this paper, we consider the joint blind source separation (JBSS) problem and introduce a number of algorithms to solve the JBSS problem using the independent vector analysis (IVA) framework. Source separation of multiple datasets simultaneously is possible when the sources within each and every dataset are independent of one another and each source is dependent on at most one source within each of the other datasets. In addition to source separation, the IVA framework solves an essential problem of JBSS, namely the identification of the dependent sources across the datasets. We propose to use the multivariate Gaussian source prior to achieve JBSS of sources that are linearly dependent across datasets. Analysis within the paper yields the local stability conditions, nonidentifiability conditions, and induced Cramer-Rao lower bound on the achievable interference to source ratio for IVA with multivariate Gaussian source priors. Additionally, by exploiting a novel nonorthogonal decoupling of the IVA cost function we introduce both Newton and quasi-Newton optimization algorithms for the general IVA framework.

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