Source reduction method using wavelets for the source separation problem

<div>In this paper we present a new criterion for bounded component analysis, a quite new approach for the Blind Source Separation problem. For the determined case, we show that the `1-norm of the estimated sources can be used as a contrast for the problem. We present a blind algorithm for the source separation of independents sources or mixtures of correlated sources by only a rotation matrix. We also present a variety of simulations assessing the performance of the proposed approach.</div>

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Solving the chaotic source separation problem

Scilight ◽

10.1063/10.0000783 ◽

2020 ◽

Vol 2020 (6) ◽

pp. 061114

Author(s):

Meeri Kim

Keyword(s):

Source Separation ◽

Separation Problem

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A general framework for extending classic array processing techniques to the underdetermined blind source separation problem

10.1109/isspa.2007.4555341 ◽

2007 ◽

Cited By ~ 1

Author(s):

Thomas Melia ◽

Scott Rickard

Keyword(s):

Blind Source Separation ◽

General Framework ◽

Array Processing ◽

Source Separation ◽

Separation Problem ◽

Underdetermined Blind Source Separation ◽

Processing Techniques

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Blind Source Separation Based on Non-Gaussianity

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.532-533.1378 ◽

2012 ◽

Vol 532-533 ◽

pp. 1378-1383

Author(s):

Bai Zhan Yang

Keyword(s):

Independent Component Analysis ◽

Blind Source Separation ◽

Source Separation ◽

Component Analysis ◽

Independent Component ◽

Time Structure ◽

Separation Problem ◽

Gaussian Source ◽

Gaussian Signal ◽

Non Gaussian

Independent component analysis is an efficient way to solve blind source separation, which has been broadly used in many fields, such as speech recognition, image processing, wireless communication system, biomedical signal processing etc. Independent component analysis for the traditional ways to solve the blind source separation problem only considers the non-Gaussian signal, without taking into account the time structure of the signal information. Proposed based on generalized self-related and non-Gaussian source separation method, the full account of the non-Gaussian signal and time structure information, to solve the blind source separation problem in the time structure of the signal. Finally, this simulation method is validated, the simulation results show that the method is effective and worthy of promotion.

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

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691310003651 ◽

2010 ◽

Vol 08 (04) ◽

pp. 575-594 ◽

Cited By ~ 2

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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