BLIND SPATIOTEMPORAL SEPARATION OF SECOND AND/OR HIGHER-ORDER CORRELATED SOURCES BY ENTROPY RATE MINIMIZATION

We propose a new entropy rate estimator for a second and/or higher-order correlated source by modeling it as the output of a linear filter, which can be mixed-phase, driven by Gaussian or non-Gaussian noise. Based on this estimator, we develop a new spatiotemporal blind source separation (BSS) algorithm, full BSS (FBSS), by minimizing the entropy rate of separated sources. FBSS provides more flexibilities in exploiting the temporal structures of sources than the state-of-the-art spatiotemporal BSS algorithms, which use AR models, and thus imply minimum-phase property of sources.