Nonorthogonal independent vector analysis using multivariate Gaussian model

Authors:
Matthew Anderson;Xi-Lin Li;Tülay Adali
Affiliations:
Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD;Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD;Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

Citing 5
Cited 6

Joint blind source separation by multiset canonical correlation analysis

IEEE Transactions on Signal Processing
Independent component analysis by entropy bound minimization

IEEE Transactions on Signal Processing
Independent vector analysis: an extension of ICA to multivariate components

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Nonorthogonal Joint Diagonalization Free of Degenerate Solution

IEEE Transactions on Signal Processing
Efficient Variant of Algorithm FastICA for Independent Component Analysis Attaining the CramÉr-Rao Lower Bound

IEEE Transactions on Neural Networks

Joint blind source separation by generalized joint diagonalization of cumulant matrices

Signal Processing
Stability of independent vector analysis

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

Signal Processing
Independent vector analysis for convolutive blind noncircular source separation

Signal Processing
A canonical correlation analysis based method for improving BSS of two related data sets

LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Finding dependent and independent components from related data sets: A generalized canonical correlation analysis based method

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of joint blind source separation of multiple datasets and introduce an effective solution to the problem. We pose the problem in an independent vector analysis (IVA) framework utilizing the multivariate Gaussian source vector distribution. We provide a new general IVA implementation using a decoupled nonorthogonal optimization algorithm and establish the connection between the new approach and another approach using second-order statistics, multiset canonical correlation analysis. Experimental results are given to demonstrate the success of the new algorithm in achieving reliable source separation for both Gaussian and non-Gaussian sources.