paper: Blind Source Separation by Sparse Decomposition

[Zibulevsky Michael]

Announcing a paper (revised version) ...


Title:    Blind Source Separation by Sparse Decomposition 
          in a Signal Dictionary

Authors:  Michael Zibulevsky and Barak A. Pearlmutter

                                Abstract
                                   
The blind source separation problem is to extract the underlying
source signals from a set of linear mixtures, where the mixing 
matrix is unknown. This situation is common, in acoustics, radio, 
medical signal and image processing, hyperspectral imaging, etc.
We suggest a two-stage separation process. First, a priori selection
of a possibly overcomplete signal dictionary (for instance a wavelet 
frame, or a learned dictionary) in which the sources are assumed
to be sparsely representable. Second, unmixing the sources by exploiting 
the their sparse representability.
We consider the general case of more sources than mixtures, but also 
derive a more efficient algorithm in the  case of a non-overcomplete 
dictionary and an equal numbers of sources and mixtures.
Experiments with artificial signals and  with musical sounds demonstrate 
significantly better separation than other known techniques.

URL of the ps file:

http://ie.technion.ac.il/~mcib/spica12.ps.gz 

Contact: michael at cs.unm.edu, bap at cs.unm.edu