Blind source separation consists on estimating n
source signals from m measurements generated through an
unknown mixing process of the sources. In the underdetermined
case where we have more sources than measurements, we divide
the problem into two stages: estimation of the mixing matrix
and inversion of the linear problem. This paper deals with the
first stage. It is well known that when the sparsity of the sources
premise is true, measurements tend to align with the columns of
the mixing matrix, so the problem can be formulated as estimating
the peaks of multidimensional probability density functions
(PDF). In this paper we analyze two different techniques to
estimate this peaks: one is to convert the multidimensional
PDF into the power spectral density (PSD) of multiple complex
sinusoidal signals and use different multidimensional espectral
estimation techniques to detect the peaks. The other is to convert
the (m − 1)- multidimensional PDF to m − 1 unidimensional
projections and estimate the peaks of these
