Temporally correlated source separation using variational Bayesian learning approach
Digital Signal Processing
Bayesian nonstationary source separation
Neurocomputing
Independent Component Analysis for Time-dependent Processes Using AR Source Model
Neural Processing Letters
Temporally correlated source separation based on variational Kalman smoother
Digital Signal Processing
Underdetermined blind source separation based on subspace representation
IEEE Transactions on Signal Processing
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This paper is devoted to the study of the second-order properties using partial autocorrelations of an instantaneous mixture of colored sources without additive noise. We introduce the notion of symmetric recursive canonical partial innovation. Then, their components, for the observation process, meet exactly with those of the source process from the order for which the autoregressive models underlying the sources are distinct. This property leads to a new separation method based on the sample counterpart of partial autocorrelation matrices associated with these innovations. Simulation results show a notable improvement of the achievements of such an approach with respect to those of similar methods. Two other separation methods related to partial autocorrelation are also discussed