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Independent signal separation using genetic algorithm
Author(s) -
Yoshioka Michifumi,
Omatu Sigeru
Publication year - 2000
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/(sici)1520-6416(200006)131:4<52::aid-eej7>3.0.co;2-t
Subject(s) - independent component analysis , signal (programming language) , divergence (linguistics) , component (thermodynamics) , separation (statistics) , algorithm , computer science , noise (video) , source separation , blind signal separation , measure (data warehouse) , genetic algorithm , separation method , signal processing , speech recognition , artificial intelligence , telecommunications , data mining , machine learning , physics , philosophy , linguistics , channel (broadcasting) , chemistry , radar , chromatography , image (mathematics) , thermodynamics , programming language
Growing multimedia systems require more efficient signal separation methods to preserve quality of voice or music recording in a noisy environment. Some signal separation methods are based on minimizing the dependence measure among input signals to separate the noise component since the noise component is usually independent of the other signals. Under such circumstances, we have developed a new method to separate independent signal components which directly minimizes the Kullback–Leibler divergence by a genetic algorithm. In this paper, we have improved the method in its separation performance and processing speed. The simulation results show that the proposed method is effective in separating the independent signals. © 2000 Scripta Technica, Electr Eng Jpn, 131(4): 52–57, 2000