  The instantaneous underdetermined audio source separation problem of
K-sensors, L-sources mixing scenario (where K < L) has been addressed by many
different approaches, provided the sources remain quite distinct in the virtual
positioning space spanned by the sensors. This problem can be tackled as a
directional clustering problem along the source position angles in the mixture.
The use of Generalised Directional Laplacian Densities (DLD) in the MDCT domain
for underdetermined source separation has been proposed before. Here, we derive
weighted mixtures of DLDs in a sparser representation of the data in the STFT
domain to perform separation. The proposed approach yields improved results
compared to our previous offering and compares favourably with the
state-of-the-art.
