Complex-valued independent vector analysis: Application to multivariate Gaussian model

Authors:
Matthew Anderson;Xi-Lin Li;TüLay Adalı
Affiliations:
Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD 21250, United States;Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD 21250, United States;Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD 21250, United States
Venue:
Signal Processing
Year:
2012

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Abstract

We consider the problem of joint blind source separation of multiple datasets and introduce a solution to the problem for complex-valued sources. We pose the problem in an independent vector analysis (IVA) framework and provide a new general IVA implementation using Wirtinger calculus and a decoupled nonunitary optimization algorithm to facilitate Newton-based optimization. Utilizing the noncircular multivariate Gaussian distribution as a source prior enables the full utilization of the complete second-order statistics available in the covariance and pseudo-covariance matrices. The algorithm provides a principled approach for achieving multiset canonical correlation analysis.