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
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;
Conference_Titel :
Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on
Print_ISBN :
0-7803-8251-X
DOI :
10.1109/ISCAS.2004.1329446