Non‐negative matrix factorisation for blind source separation in wavelet transform domain

2015 ◽  
Vol 9 (2) ◽  
pp. 111-119 ◽  
Author(s):  
Jamel Hattay ◽  
Samir Belaid ◽  
Wady Naanaa
Keyword(s):  
Wavelet Transform ◽  
Blind Source Separation ◽  
Source Separation ◽  
Transform Domain ◽  
Matrix Factorisation

scholarly journals A New Blind Source Separation Algorithm Framework for Noisy Mixing Model Based on the Energy Concentration Characteristic in Signal Transform Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jiong Li ◽  
Lu Feng
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Signal Separation ◽  
Energy Concentration ◽  
Transform Domain ◽  
Transform Method ◽  
Analysis Process ◽  
Algorithm Framework ◽  
Concentration Characteristic ◽  
High Level

Blind source separation is a widely used technique to analyze multichannel data. In most real-world applications, noise is inevitable and will affect the quality of signal separation and even make signal separation failure. In this paper, a new signal processing framework is proposed to separate noisy mixing sources. It is composed of two different stages. The first step is to process the mixing signal by a certain signal transform method to make the expected signals have energy concentration characteristics in the transform domain. The second stage is formed by a certain BSS algorithm estimating the demixing matrix in the transform domain. In the energy concentration segment, the SNR can reach a high level so that the demixing matrix can be estimated accurately. The analysis process of the proposed algorithm framework is analyzed by taking the wavelet transform as an example. Numerical experiments demonstrate the efficiency of the proposed algorithm to estimate the mixing matrix in comparison with well-known algorithms.

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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