DocumentCode :
2488707
Title :
Complex ICA-R
Author :
Rajapakse, Jagath C. ; Chen, Wenda
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
8
Abstract :
The Complex Independent Component Analysis (CICA) which extends Independent Component Analysis (ICA) to complex signals has found applications in various fields. The ICA with Reference (ICA-R) has recently gained popularity in semi-blind separation of signals when a priori information of the desired sources are available in the form of reference signals. This paper extends the framework of ICA-R to complex signals and demonstrates the use of Complex ICA-R (CICA-R) with applications to both synthetic data and real speech data. Our experiments indicate that CICA-R is more effective than ICA, ICA-R, or CICA, in separation of complex signals when reference signals relating to source signals are available.
Keywords :
blind source separation; independent component analysis; ICA; complex independent component analysis; reference signal; semiblind signal separation; Data mining; Electroencephalography; Frequency domain analysis; Independent component analysis; Signal to noise ratio; Speech; Time domain analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location :
Barcelona
ISSN :
1098-7576
Print_ISBN :
978-1-4244-6916-1
Type :
conf
DOI :
10.1109/IJCNN.2010.5596453
Filename :
5596453
Link To Document :
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