New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources

Xu, Pengcheng; Shen, Yuehong; Li, Hui

doi:10.13164/re.2015.0262

Cited by 1 publication

(2 citation statements)

References 35 publications

(56 reference statements)

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“…On the 6 th stage, transform values are obtained from best x into the separation vector. In the 7 th stage, the orthogonalization process is performed via equation (14).…”

Section: Bss Based On Hepso Algorithmmentioning

confidence: 99%

“…1 and Alg. 2, to maximize it [14]. Jiangong WANG et al have proposed a BSS algorithm which is based on linear autocorrelations and joint diagonalization (LAJD), done through a merging diagonalizer of several average delayed covariance matrices at the optimal time delay [15].Yongjian ZHAO et al have proposed a framework using a sequential method based on second order statistics [16].…”

Section: Introductionmentioning

confidence: 99%

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Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

2019

KSII TIIS

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Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.

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“…On the 6 th stage, transform values are obtained from best x into the separation vector. In the 7 th stage, the orthogonalization process is performed via equation (14).…”

Section: Bss Based On Hepso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

2019

KSII TIIS

View full text Add to dashboard Cite

show abstract

New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources

Cited by 1 publication

References 35 publications

Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

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