scholarly journals Source separation and localisation via tensor decomposition for distributed arrays

2019 ◽  
Vol 2019 (20) ◽  
pp. 6616-6619
Author(s):  
Yuanbing Cheng ◽  
Yapeng He
Keyword(s):  
Source Separation ◽  
Tensor Decomposition ◽  
Distributed Arrays

scholarly journals Alternating Group Lasso for Block-Term Tensor Decomposition and Application to ECG Source Separation

2020 ◽  
Vol 68 ◽  
pp. 2682-2696 ◽  
Author(s):  
Jose Henrique de Morais Goulart ◽  
Pedro Marinho Ramos de Oliveira ◽  
Rodrigo Cabral Farias ◽  
Vicente Zarzoso ◽  
Pierre Comon
Keyword(s):  
Source Separation ◽  
Tensor Decomposition ◽  
Group Lasso ◽  
Alternating Group

scholarly journals Underdetermined Blind Source Separation Combining Tensor Decomposition and Nonnegative Matrix Factorization

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 521 ◽  
Author(s):  
Yuan Xie ◽  
Kan Xie ◽  
Junjie Yang ◽  
Shengli Xie
Keyword(s):  
Blind Source Separation ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Source Separation ◽  
Nonnegative Matrix ◽  
Tensor Decomposition ◽  
Model Parameters ◽  
Separation Performance ◽  
Underdetermined Blind Source Separation ◽  
Source Signals

Underdetermined blind source separation (UBSS) is a hot topic in signal processing, which aims at recovering the source signals from a number of observed mixtures without knowing the mixing system. Recently, expectation-maximization algorithm shows a great potential in the UBSS. However, the final separation results depend strongly on the parameter initialization, leading to poor separation performance. In this paper, we propose an effective algorithm that combines tensor decomposition and nonnegative matrix factorization (NMF). In the proposed algorithm, we first employ tensor decomposition to estimate the mixing matrix, and NMF source model is used to estimate the source spectrogram factors. Then a series of iterations are derived to update the model parameters. At the same time, the spatial images of source signals are estimated with Wiener filters constructed from the learned parameters. Therefore, time-domain sources can be obtained through inverse short-time Fourier transform. Finally, plenty of experimental results demonstrate the effectiveness and advantages of our proposed algorithm over the compared algorithms.

Target Detection in a Scenario of Natural Sounds: The Role of Spatial Sound Source Separation

2013 ◽  
Author(s):  
Susanne Mayr ◽  
Gunnar Regenbrecht ◽  
Kathrin Lange ◽  
Albertgeorg Lang ◽  
Axel Buchner
Keyword(s):  
Target Detection ◽  
Sound Source ◽  
Source Separation ◽  
Natural Sounds ◽  
Sound Source Separation ◽  
Spatial Sound
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