Sparse Bayesian learning algorithm for separable dictionaries

2021 ◽  
Vol 111 ◽  
pp. 102990
Author(s):  
Andra Băltoiu ◽  
Bogdan Dumitrescu
Keyword(s):  
Bayesian Learning ◽  
Learning Algorithm ◽  
Sparse Bayesian Learning

scholarly journals A GAMP-Based Low Complexity Sparse Bayesian Learning Algorithm

2018 ◽  
Vol 66 (2) ◽  
pp. 294-308 ◽  
Author(s):  
Maher Al-Shoukairi ◽  
Philip Schniter ◽  
Bhaskar D. Rao
Keyword(s):  
Bayesian Learning ◽  
Learning Algorithm ◽  
Low Complexity ◽  
Sparse Bayesian Learning

CFAR Block-Sparse Bayesian Learning Algorithm for the Off-grid DOA Estimation with Coprime Array

2019 ◽  
Vol 28 (4) ◽  
pp. 863-870
Author(s):  
Jun HAN ◽  
Minghao HE ◽  
Mingyue FENG ◽  
Ying JIANG
Keyword(s):  
Bayesian Learning ◽  
Learning Algorithm ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Coprime Array

Guided-wave signal processing by the sparse Bayesian learning approach employing Gabor pulse model

2016 ◽  
Vol 16 (3) ◽  
pp. 347-362 ◽  
Author(s):  
Biao Wu ◽  
Yong Huang ◽  
Xiang Chen ◽  
Sridhar Krishnaswamy ◽  
Hui Li
Keyword(s):  
Damage Detection ◽  
Bayesian Learning ◽  
Guided Waves ◽  
Learning Algorithm ◽  
Guided Wave ◽  
Sparse Bayesian Learning ◽  
Mode Identification ◽  
Wave Signal ◽  
Gabor Dictionary ◽  
The Many

Guided waves have been used for structural health monitoring to detect damage or defects in structures. However, guided wave signals often involve multiple modes and noise. Extracting meaningful damage information from the received guided wave signal becomes very challenging, especially when some of the modes overlap. The aim of this study is to develop an effective way to deal with noisy guided-wave signals for damage detection as well as for de-noising. To achieve this goal, a robust sparse Bayesian learning algorithm is adopted. One of the many merits of this technique is its good performance against noise. First, a Gabor dictionary is designed based on the information of the noisy signal. Each atom of this dictionary is a modulated Gaussian pulse. Then the robust sparse Bayesian learning technique is used to efficiently decompose the guided wave signal. After signal decomposition, a two-step matching scheme is proposed to extract meaningful waveforms for damage detection and localization. Results from numerical simulations and experiments on isotropic aluminum plate structures are presented to verify the effectiveness of the proposed approach in mode identification and signal de-noising for damage detection.

scholarly journals Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Zhe Wang ◽  
Luyun Wang ◽  
Xiumei Li ◽  
Lifan Zhao ◽  
Guoan Bi
Keyword(s):  
Bayesian Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Second Step ◽  
Sparse Bayesian Learning ◽  
Transform Domain ◽  
Separation Problem ◽  
Efficient Data ◽  
Mixing Matrix ◽  
Speech Mixtures

This paper describes a novel algorithm for underdetermined speech separation problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The proposed algorithm consists of two steps. The unknown mixing matrix is firstly estimated from the speech mixtures in the transform domain by using K-means clustering algorithm. In the second step, the speech sources are recovered based on an autocalibration sparse Bayesian learning algorithm for speech signal. Numerical experiments including the comparison with other sparse representation approaches are provided to show the achieved performance improvement.

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