new paper: Blind Source Separation by Sparse Decomposition

[Zibulevsky Michael]

Announcing a new paper....

Title:    Blind Source Separation by Sparse Decomposition

Authors:  Michael Zibulevsky and Barak A. Pearlmutter

                                Abstract
                                   
The blind source separation problem is to extract the underlying
source signals from a set of their linear mixtures, where the mixing
matrix is unknown. This situation is common, eg in acoustics, radio, and
medical signal processing. We exploit the  property of the sources to
have a sparse representation in a corresponding (possibly overcomplete)
signal dictionary. Such a dictionary may consist of wavelets, wavelet
packets, etc., or be obtained by learning from  a given family of
signals. Starting from  the maximum posteriori framework, which is
applicable to the case of more sources than mixtures, we derive a few
other categories of objective functions, which provide faster and more
robust computations, when there are an equal number of sources and
mixtures. Our experiments with artificial signals and  with musical
sounds demonstrate significantly better separation than other known
techniques.


URL of the ps file:

http://iew3.technion.ac.il:8080/~mcib/ 


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