Blind Source Separation Based on Space-time-frequency Diversity

We investigate the assumption that sources have disjoint support in the time domain, time-frequency domain, or frequency domain. We call such signals disjoint orthogonal. The class of signals that approximately satisfies this assumption includes many synthetic signals, music and speech, as well as some biological signals. We measure the disjoint orthogonality of the benchmark signals in the ICALAB Toolbox in the time, time-frequency, and frequency domains and show that most satisfy the assumption in at least one representation. In order to compare this assumption with other common source assumptions, we derive a demixing algorithm for noisy instantaneous mixtures based on disjoint orthogonality and compare its performance to the algorithms in the ICALAB Toolbox, all of which rely on the second-order statistics, non-stationarity, or higherorder statistics of the sources. The results indicate that space-timefrequency diversity is a useful assumption for the design of BSS/ICA algorithms.