By Topic

Approximate maximum likelihood source separation using the natural gradient

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Seungjin Choi ; Dept. of Comput. Sci. & Eng., POSTECH, South Korea ; Cichocki, A. ; Liqing Zhang ; Amari, S.

This paper addresses a maximum likelihood approach to source separation in the case of overdetermined mixtures corrupted by additive white Gaussian noise. We present an objective function that is an approximate likelihood function based on the Laplace approximation. Then we derive a natural gradient adaptation algorithm which maximizes the corresponding approximate likelihood function. Useful behavior of the proposed method is verified by numerical experiments

Published in:

Wireless Communications, 2001. (SPAWC '01). 2001 IEEE Third Workshop on Signal Processing Advances in

Date of Conference: