Probabilistic Formulation of Independent Vector Analysis Using Complex Gaussian Scale Mixtures
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Modeling and estimation of dependent subspaces with non-radially symmetric and skewed densities
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Blind vector deconvolution: convolutive mixture models in short-time fourier transform domain
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Real-time independent vector analysis for convolutive blind source separation
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Nonorthogonal independent vector analysis using multivariate Gaussian model
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Contrast functions for independent subspace analysis
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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In this paper, we solve an ICA problem where both source and observation signals are multivariate, thus, vectorized signals. To derive the algorithm, we define dependence between vectors as Kullback-Leibler divergence between joint probability and the product of marginal probabilities, and propose a vector density model that has a variance dependency within a source vector. The example shows that the algorithm successfully recovers the sources and it does not cause any permutation ambiguities within the sources. Finally, we propose the frequency domain blind source separation (BSS) for convolutive mixtures as an application of IVA, which separates 6 speeches with 6 microphones in a reverberant room environment.