Comment on "Blind source separation based on endpoint estimation with applications to the MLSP 2006 data competition"

This note illustrates some shortcomings of the criterion proposed in “Blind source separation based on endpoint estimation with applications to the MLSP 2006 data competition” when the number of samples is finite. This algorithm considers mutually independent sources with semibounded support, however, even for a sufficient sample size for which the finite bound of the support of the density of the output can be estimated accurately, the endpoint estimate nearest to the mean might be in the unbounded side of this density. In that case, the superadditivity of the least absolute endpoint estimate is usually violated, causing the loss of the contrast function property and of the capability of discriminating hidden sources, for practical versions of this criterion.