Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

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 b...

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Bibliographic Details
Published in:KSII transactions on Internet and information systems 2019-05, Vol.13 (5), p.2574-2587
Main Authors: KHALFA, Ali, AMARDJIA, Nourredine, KENANE, Elhadi, CHIKOUCHE, Djamel, ATTIA, Abdelouahab
Format: Article
Language:Korean
Subjects:
Artificial Bee Colony
Blind Source Separation
BSS demo
Multi-crossover
Particle Swarm Optimization
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Summary: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.
ISSN:1976-7277
1976-7277