Paper Info
Title
Joint blind source separation by generalized joint diagonalization of cumulant matrices
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.
Year
DOI
Venue
2011
10.1016/j.sigpro.2011.04.016
Signal Processing
Keywords
Field
DocType
new application,cumulant matrix,independent vector analysis,joint blind source separation (jbss),canonical correlation analysis (cca),correlation matrix,new algorithm,fourth-order cumulant matrix,multiset cca (mcca),independent vector analysis (iva),generalized joint diagonalization,joint blind source separation,real-world data,multiset canonical correlation analysis,orthogonal procrustes problem,blind source separation,canonical correlation analysis,cumulant
Signal processing,Heartbeat,Mathematical optimization,Combinatorics,Iterative method,Multiset,Matrix (mathematics),Canonical correlation,Algorithm,Covariance matrix,Blind signal separation,Mathematics
Journal
Volume
Issue
ISSN
91
10
Signal Processing
Citations 
PageRank 
References 
36
1.32
17
Authors
3
Name
Order
Citations
PageRank
Xi-Lin Li154734.85
Tülay Adalı227614.54
Matthew Anderson326314.64