Study for blind source separation on time‐frequency domain considering phase information

2006 ◽  
Vol 120 (5) ◽  
pp. 3047-3047
Author(s):  
Fumio Sasaki ◽  
Masahito Yasuoka ◽  
Osamu Tanaka
Keyword(s):  
Frequency Domain ◽  
Blind Source Separation ◽  
Source Separation ◽  
Phase Information ◽  
Time Frequency

Blind Source Separation Based on Wavelet and Cross-Wavelet

2011 ◽  
Vol 328-330 ◽  
pp. 2064-2068 ◽  
Author(s):  
Jing Hui Wang ◽  
Yuan Chao Zhao
Keyword(s):  
Frequency Domain ◽  
Blind Source Separation ◽  
Time Scale ◽  
Source Separation ◽  
Blind Separation ◽  
Time Frequency ◽  
Non Stationary Signal ◽  
Cross Wavelet

In this paper, a novel blind separation approach using wavelet and cross-wavelet is presented. This method extends the separate technology from time-frequency domain to time-scale domain. The simulation showed that this method is suitable for dealing with non-stationary signal.

BLIND SOURCE SEPARATION OF SPATIO-TEMPORAL MIXED SIGNALS USING PHASE INFORMATION OF ANALYTIC WAVELET TRANSFORM

2010 ◽  
Vol 08 (04) ◽  
pp. 575-594 ◽  
Author(s):  
RYUICHI ASHINO ◽  
TAKESHI MANDAI ◽  
AKIRA MORIMOTO
Keyword(s):  
Wavelet Transform ◽  
Blind Source Separation ◽  
Source Separation ◽  
Cocktail Party ◽  
Phase Information ◽  
Separation Problem ◽  
Time Frequency ◽  
Cocktail Party Problem ◽  
Analytic Wavelet ◽  
Spatio Temporal

The cocktail party problem deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noises. The blind source separation problem is how to enable computers to solve the cocktail party problem in a satisfactory manner. The simplest version of spatio-temporal mixture problem, which is a type of blind source separation problem, has been solved by a generalized version of the quotient signal estimation method based on the analytic wavelet transform, under the assumption that the time delays are integer multiples of the sampling period. The analytic wavelet transform is used to represent time-frequency information of observed signals. Without the above assumption, improved algorithms, utilizing phase information of the analytic wavelet transforms of the observed signals, are proposed. A series of numerical simulations is presented.

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