Underdetermined joint blind source separation based on tensor decomposition

2016 ◽  
Author(s):  
Liang Zou ◽  
Z. Jane Wang ◽  
Xun Chen ◽  
Xiangyang Ji
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Tensor Decomposition

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.

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