Blind source separation of convolved sources by joint approximate diagonalization of cross-spectral density matrices

2002 ◽  
Author(s):  
K. Rahbar ◽  
J.P. Reilly
Keyword(s):  
Spectral Density ◽  
Blind Source Separation ◽  
Source Separation ◽  
Density Matrices ◽  
Cross Spectral Density

On genuine cross-spectral density matrices

2009 ◽  
Vol 11 (8) ◽  
pp. 085706 ◽  
Author(s):  
F Gori ◽  
V Ramírez-Sánchez ◽  
M Santarsiero ◽  
T Shirai
Keyword(s):  
Spectral Density ◽  
Density Matrices ◽  
Cross Spectral Density

Application of Power Spectral Density on Estimation of the Number of Source Signals

2013 ◽  
Vol 328 ◽  
pp. 604-608
Author(s):  
Ning Li ◽  
Hai Ting Chen ◽  
Shao Peng Liu
Keyword(s):  
Theoretical Analysis ◽  
Complex Systems ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Blind Source Separation ◽  
Source Separation ◽  
Power Spectral ◽  
Source Signals

Blind source separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the complex systems. A new estimating method based on power spectral density (PSD) is presented. When the relation between the number of sensors and that of sources is unknown, the PSD matrix is first obtained by the ratio of PSD of the observation signals, and then the number of source signals can be estimated by clustering the column vectors of PSD matrix. The effectiveness of the proposed method is verified by theoretical analysis and experiments.

Elementary-field expansions of genuine cross-spectral density matrices

2009 ◽  
Vol 34 (15) ◽  
pp. 2303 ◽  
Author(s):  
Rosario Martínez-Herrero ◽  
Pedro M. Mejías
Keyword(s):  
Spectral Density ◽  
Density Matrices ◽  
Cross Spectral Density ◽  
Elementary Field
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