This report focuses on the blind 
  separation of N sources from M mixtures when
  the underlying system is underdetermined
  (i.e., when M<N), as applied to mixtures with only
  attenuations and delays. The separation is realized
  in the frequency domain where, at least for speech and music signals,
  the representation is sparse. The matrix of
  relative attenuations is infered by angular clustering
  of the magnitude of the input, yielding a
  coarse partition of the data into their nearest sources.
  For each partition, then, the differential delay
  is infered by scattering the real and 
  imaginary components until the cluster reappears.
  Given the attenuation and delay matrices,
  the sources are infered at each frequency bin
  by assuming that the magnitude of the 
  spectral coefficients is laplacian distributed,
  which leads to the minimization of the sum of magnitudes,
  subject to the mixing equations. The resulting
  problem is an instance of second-order cone 
  programming.

  The approach is illustrated here for M=2 sensors and N
  up to six speech and music sources, using synthetic
  mixtures simulating real acoustic scenarios without
  reverberation.