Blind source separation by sparse decomposition

Michael Zibulevsky; Barak A. Pearlmutter

doi:10.1117/12.381678

5 April 2000 Blind source separation by sparse decomposition

Michael Zibulevsky, Barak A. Pearlmutter

Proceedings Volume 4056, Wavelet Applications VII; (2000) https://doi.org/10.1117/12.381678
Event: AeroSense 2000, 2000, Orlando, FL, United States

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 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 a 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.

Citation Download Citation

Michael Zibulevsky and Barak A. Pearlmutter "Blind source separation by sparse decomposition", Proc. SPIE 4056, Wavelet Applications VII, (5 April 2000); https://doi.org/10.1117/12.381678

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available