scholarly journals Antisparsity as a Prior in Blind Separation of Correlated Sources

2021 ◽  
Author(s):  
Renan Brotto ◽  
Kenji Nose-Filho ◽  
João M. T. Romano
Keyword(s):  
Wireless Communication ◽  
Blind Source Separation ◽  
Source Separation ◽  
Constant Modulus ◽  
Blind Separation ◽  
Constant Modulus Algorithm ◽  
Computational Simulations ◽  
Correlated Sources ◽  
Simulation Results ◽  
Binary Sources

<div>This letter introduces the concept of antisparse Blind Source Separation (BSS), proposing a suitable criterion based on the $\ell_\infty$ norm to explore the antisparsity feature. </div><div>The effectiveness of the criterion is theoretically demonstrated and it is also evaluated by computational simulations, which consider up to ten distinct sources with different correlation levels. Moreover, we simulated a scenario in wireless communication with binary sources, comparing our approach to the Constant Modulus algorithm. Both the theoretical and the simulation results highlight the potentiality of using antisparsity as a prior in BSS.</div>

scholarly journals Antisparsity as a Prior in Blind Separation of Correlated Sources

2021 ◽  
Author(s):  
Renan Brotto ◽  
Kenji Nose-Filho ◽  
João M. T. Romano
Keyword(s):  
Wireless Communication ◽  
Blind Source Separation ◽  
Source Separation ◽  
Constant Modulus ◽  
Blind Separation ◽  
Constant Modulus Algorithm ◽  
Computational Simulations ◽  
Correlated Sources ◽  
Simulation Results ◽  
Binary Sources

<div>This letter introduces the concept of antisparse Blind Source Separation (BSS), proposing a suitable criterion based on the $\ell_\infty$ norm to explore the antisparsity feature. </div><div>The effectiveness of the criterion is theoretically demonstrated and it is also evaluated by computational simulations, which consider up to ten distinct sources with different correlation levels. Moreover, we simulated a scenario in wireless communication with binary sources, comparing our approach to the Constant Modulus algorithm. Both the theoretical and the simulation results highlight the potentiality of using antisparsity as a prior in BSS.</div>

Blind Complex Source Separation Based on Cyclostationary Statistics

2014 ◽  
Vol 519-520 ◽  
pp. 1051-1056
Author(s):  
Jie Guo ◽  
An Quan Wei ◽  
Lei Tang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Separation Performance ◽  
Separation Algorithm ◽  
Complex Signals ◽  
Cyclic Frequency ◽  
Complex Source ◽  
Simulation Results ◽  
Better Than ◽  
Separation Model

This paper analyzed a blind source separation algorithm based on cyclic frequency of complex signals. Under the blind source separation model, we firstly gave several useful assumptions. Then we discussed the derivation of the BSS algorithm, including the complex signals and the normalization situation. Later, we analyzed the complex WCW-CS algorithm, which was compared with NGA, NEASI and NGA-CS algorithms. Simulation results show that the complex WCW-CS algorithm has the best convergence and separation performance. It can also effectively separate mixed image signals, whose performance was better than NGA algorithm.

Blind Source Separation Based on Wavelet and Cross-Wavelet

2011 ◽  
Vol 328-330 ◽  
pp. 2064-2068 ◽  
Author(s):  
Jing Hui Wang ◽  
Yuan Chao Zhao
Keyword(s):  
Frequency Domain ◽  
Blind Source Separation ◽  
Time Scale ◽  
Source Separation ◽  
Blind Separation ◽  
Time Frequency ◽  
Non Stationary Signal ◽  
Cross Wavelet

In this paper, a novel blind separation approach using wavelet and cross-wavelet is presented. This method extends the separate technology from time-frequency domain to time-scale domain. The simulation showed that this method is suitable for dealing with non-stationary signal.

scholarly journals A New Criterion for Bounded Component Analysis

2021 ◽  
Author(s):  
Renan Brotto ◽  
Kenji Nose-Filho ◽  
João M. T. Romano
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Rotation Matrix ◽  
Separation Problem ◽  
Correlated Sources ◽  
New Approach ◽  
Bounded Component ◽  
New Criterion

<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>

scholarly journals BLIND SOURCE SEPARATION OF AUDIO SIGNALS USING WVD-KR ALGORITHM

2014 ◽  
pp. 226-230
Author(s):  
D. SUGUMAR ◽  
NEETHU SUSAN RAJAN ◽  
P. T. VANATHI
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Improved Method ◽  
Audio Signals ◽  
Time Frequency ◽  
Mixing Matrix ◽  
Simulation Results ◽  
Stationary Sources

Under-determined blind source separation aims to separate N non-stationary sources from M (M<N) mixtures.Paper presents a time-frequency approach (TF) to under-determined blind source separation of N non-stationary sources from M mixtures(M<N). It is based on Wigner-Ville distribution and Khatri-Rao product. Improved method involves a two step approach which involves the estimation of the mixing matrix where negative values of auto WVD of the sources are fully considered and secondly auto-term TF points are extracted.After extracting the auto-term TF points source WVD values at every TF point are computed using a new algorithm based on Khatri-Rao product. Thus sources are separated with the proposed approach no matter how many active sources there are as long as N≤ 2M-1.Simulation results are presented to show the superiority of the proposed algorithm by comparing it with the existing algorithms.

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