DocumentCode :
3529091
Title :
Second-order improperness in frequency-domain colored signal model
Author :
Cong, Fengyu ; Ristaniemi, Tapani
Author_Institution :
Sch. of Inf. Technol., Univ. of Jyvaskyla, Jyvaskyla
fYear :
2008
fDate :
16-19 Oct. 2008
Firstpage :
321
Lastpage :
326
Abstract :
This study discusses the second-order improperness in the frequency-domain colored signal model. After the real-valued and colored signal in the time-domain is transformed into the frequency domain, the convolutive blind source separation (BSS) methods may generate a window indexed vector, and minimum variance distortionless response (MVDR) beamforming may produce an array sensor indexed vector, and the trial structured EEG data set may provide a trial-indexed vector. These three complex-valued vectors have such similarities: 1) despite of resulting from the Fourier transformation, they are not indexed by the frequency bins; 2) its real part and image part are proved to be correlated. Hence, such a complex-valued vector is improper, and the pseudo-autocorrelation matrix of this improper vector exists. Moreover, if components of an improper complex-valued vector are uncorrelated, except the autocorrelation matrix, the pseudo-autocorrelation matrix should also be diagonal. This one more statistics could be useful to design better the complex-valued BSS algorithm and MVDR beamforming algorithm.
Keywords :
Fourier transforms; array signal processing; blind source separation; correlation methods; frequency-domain analysis; matrix algebra; time-domain analysis; vectors; Fourier transformation; MVDR beamforming algorithm; array sensor indexed vector; complex-valued BSS algorithm; complex-valued vectors; convolutive blind source separation methods; frequency bins; frequency-domain colored signal model; minimum variance distortionless response beamforming; pseudo-autocorrelation matrix; real-valued signal; second-order improperness; time-domain; trial structured EEG data set; trial-indexed vector; window indexed vector; Array signal processing; Blind source separation; Brain modeling; Distortion; Electroencephalography; Frequency domain analysis; Sensor arrays; Signal generators; Source separation; Time domain analysis; Frequency-domain colored signal model; second order improperness; sensor indexed vector; trial indexed vector; window indexed vector;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing, 2008. MLSP 2008. IEEE Workshop on
Conference_Location :
Cancun
ISSN :
1551-2541
Print_ISBN :
978-1-4244-2375-0
Electronic_ISBN :
1551-2541
Type :
conf
DOI :
10.1109/MLSP.2008.4685500
Filename :
4685500
Link To Document :
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