Temporally correlated source separation using variational Bayesian learning approach
Digital Signal Processing
A null space method for over-complete blind source separation
Computational Statistics & Data Analysis
Bayesian nonstationary source separation
Neurocomputing
Temporally correlated source separation based on variational Kalman smoother
Digital Signal Processing
Bayesian separation of images modeled with MRFs using MCMC
IEEE Transactions on Image Processing
Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields
IEEE Transactions on Image Processing
Blind separation of non-stationary images using Markov models
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
An EM method for spatio-temporal blind source separation using an AR-MOG source model
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Markovian blind image separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Convolutive demixing with sparse discrete prior models for markov sources
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Variational bayesian method for temporally correlated source separation
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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A maximum likelihood (ML) approach is used to separate the instantaneous mixtures of temporally correlated, independent sources with neither preliminary transformation nor a priori assumption about the probability distribution of the sources. A Markov model is used to represent the joint probability density of successive samples of each source. The joint probability density functions are estimated from the observations using a kernel method. For the special case of autoregressive models, the theoretical performance of the algorithm is computed and compared with the performance of second-order algorithms and i.i.d.-based separation algorithms.