DocumentCode
418558
Title
Blind separation with Gaussian mixture model for convolutively mixed sources
Author
Ohata, Masashi ; Mukai, Toshiharu ; Matsuoka, Kiyotoshi
Author_Institution
Biologically Integrative Sensors Lab., RIKEN BMC Res. Center, Nagoya, Japan
Volume
5
fYear
2004
fDate
23-26 May 2004
Abstract
This paper proposes an online blind separation algorithm with Gaussian mixture model for convolutively mixed sources. Although similar algorithms were proposed, they were derived for independent and identically distributed (iid) sources. They may not work for sources which are not made iid by any linear filter. From the theoretical viewpoint, our algorithm also works well for the sources and search for an optimal separator simultaneously, it can be applied to the case where their statistical properties are quite unknown, except that sources are nonGaussian.
Keywords
Gaussian noise; blind source separation; convolution; Gaussian mixture model; convolutively mixed sources; identically distributed sources; iid sources; independent distributed sources; linear filters; nonGaussian source; online blind separation algorithm; optimal separator; statistical properties; Biological system modeling; Biology; Biosensors; Blind source separation; Brain modeling; Electronic mail; Neural networks; Nonlinear filters; Particle separators; Source separation;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on
Print_ISBN
0-7803-8251-X
Type
conf
DOI
10.1109/ISCAS.2004.1329446
Filename
1329446
Link To Document