Neural Computation
An analysis of entropy estimators for blind source separation
Signal Processing
IEEE Transactions on Signal Processing
Signal separation by symmetric adaptive decorrelation: stability,convergence, and uniqueness
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Independent Component Analysis for Time-dependent Processes Using AR Source Model
Neural Processing Letters
Hi-index | 0.00 |
A maximum likelihood blind source separation algorithm is developed. The temporal dependencies are explained by assuming that each source is an AR process and the distribution of the associated i.i.d. innovations process is described using a Mixture of Gaussians (MOG). Unlike most maximum likelihood methods the proposed algorithm takes into account both spatial and temporal information, optimization is performed using the Expectation-Maximization method, and the source model is learned along with the demixing parameters.