Blind source separation using joint signal representations for arbitrary variables

Blind source separation is an emerging field of fundamental research with a broad range of applications. It is motivated by practical problems that involve several source signals and several sensors. Each sensor receives an instantaneous linear mixture of the source signals. The problem of the blind source separation consists then of recovering the original waveforms of the sources without any knowledge of the mixture structure. So far, the problem of the blind source separation has been solved using statistical information available on the source signals. A blind source separation approach for non-stationary signals based on time- frequency representations (TFR) have been recently introduced by the authors (SPIE 1996). Herein, we generalize the TFR based blind source separation approach to arbitrary variables, including time and frequency. 'Spatial joint arbitrary variable distributions' are introduced and used for blind source separation via joint diagonalization techniques.