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

Author(s):  
RYUICHI ASHINO ◽  
TAKESHI MANDAI ◽  
AKIRA MORIMOTO

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.

2009 ◽  
Vol 88 (3) ◽  
pp. 425-456 ◽  
Author(s):  
Ryuichi Ashino ◽  
Takeshi Mandai ◽  
Akira Morimoto ◽  
Fumio Sasaki

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gang Yu

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.


2007 ◽  
Vol 187 (1) ◽  
pp. 153-162 ◽  
Author(s):  
Keiko Fujita ◽  
Yoshitsugu Takei ◽  
Akira Morimoto ◽  
Ryuichi Ashino

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