DocumentCode
1375484
Title
Neural network for demixing super-Gaussian signals
Author
Prieto, B. ; Prieto, A. ; Puntonet, C.G. ; Martin-Smith, P.
Author_Institution
Dept. de Arquitectura y Tecnologia de Computadores, Granada Univ., Spain
Volume
36
Issue
17
fYear
2000
fDate
8/17/2000 12:00:00 AM
Firstpage
1474
Lastpage
1475
Abstract
A new method for separating linear mixtures of statistically independent signals with super-Gaussian probability distributions, using a simple neural network, is proposed. The procedure is based on geometric properties, and it is shown that the maxima of the mixed density distribution belong to straight lines, the direction vectors of which, when taken as columns of a matrix, comprise a demixing matrix. The results obtained with synthetic mixtures of real speech signals are shown
Keywords
signal processing; neural network; signal demixing; speech separation; super-Gaussian probability distribution;
fLanguage
English
Journal_Title
Electronics Letters
Publisher
iet
ISSN
0013-5194
Type
jour
DOI
10.1049/el:20001053
Filename
865046
Link To Document