Joint blind source separation by generalized joint diagonalization of cumulant matrices

Authors:
Xi-Lin Li;Tülay Adalı;Matthew Anderson
Affiliations:
Department of CSEE, UMBC, Baltimore, MD 21250, United States;Department of CSEE, UMBC, Baltimore, MD 21250, United States;Department of CSEE, UMBC, Baltimore, MD 21250, United States
Venue:
Signal Processing
Year:
2011

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Abstract

In this paper, we show that the joint blind source separation (JBSS) problem can be solved by jointly diagonalizing cumulant matrices of any order higher than one, including the correlation matrices and the fourth-order cumulant matrices. We introduce an efficient iterative generalized joint diagonalization algorithm such that a series of orthogonal procrustes problems are solved. We present simulation results to show that the new algorithms can reliably solve the permutation ambiguity in JBSS and that they offer superior performance compared with existing multiset canonical correlation analysis (MCCA) and independent vector analysis (IVA) approaches. Experiment on real-world data for separation of fetal heartbeat in electrocardiogram (ECG) data demonstrates a new application of JBSS, and the success of the new algorithms for a real-world problem.