  In this paper, both non-mixing and mixing local minima of the entropy are
analyzed from the viewpoint of blind source separation (BSS); they correspond
respectively to acceptable and spurious solutions of the BSS problem. The
contribution of this work is twofold. First, a Taylor development is used to
show that the \textit{exact} output entropy cost function has a non-mixing
minimum when this output is proportional to \textit{any} of the non-Gaussian
sources, and not only when the output is proportional to the lowest entropic
source. Second, in order to prove that mixing entropy minima exist when the
source densities are strongly multimodal, an entropy approximator is proposed.
The latter has the major advantage that an error bound can be provided. Even if
this approximator (and the associated bound) is used here in the BSS context,
it can be applied for estimating the entropy of any random variable with
multimodal density.
