  A novel blind estimate of the number of sources from noisy, linear mixtures
is proposed. Based on Sz\'ekely et al.'s distance correlation measure, we
define the Sources' Dependency Criterion (SDC), from which our estimate arises.
Unlike most previously proposed estimates, the SDC estimate exploits the full
independence of the sources and noise, as well as the non-Gaussianity of the
sources (as opposed to the Gaussianity of the noise), via implicit use of
high-order statistics. This leads to a more robust, resilient and stable
estimate w.r.t. the mixing matrix and the noise covariance structure. Empirical
simulation results demonstrate these virtues, on top of superior performance in
comparison with current state of the art estimates.
