A fast asymptotically efficient algorithm for blind separation of a linear mixture of block-wise stationary autoregressive processes

Authors:
Petr Tichavsky;Arie Yeredor;Zbynek Koldovsky
Affiliations:
Institute of Information Theory and Automation, P.O.Box 18, 182 08 Prague 8, Czech Republic;School of Electrical Engineering, Tel-Aviv University, P.O.Box 39040, 69978, ISRAEL;Institute of Information Theory and Automation, P.O.Box 18, 182 08 Prague 8, Czech Republic
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

We propose a novel blind source separation algorithm called Block AutoRegressive Blind Identification (BARBI). The algorithm is asymptotically efficient in separation of instantaneous linear mixtures of blockwise stationary Gaussian autoregressive processes. A novel closed-form formula is derived for a Cramér Rao lower bound on elements of the corresponding Interference-to-Signal Ratio (ISR) matrix. This theoretical ISR matrix can serve as an estimate of the separation performance on the particular data. In simulations, the algorithm is shown to be applicable in blind separation of a linear mixture of speech signals.