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Independent Component Analysis Using Time Delayed Sampling
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.860752IEEE-INNS-ENNS International Joint Co ...
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Michifumi Yoshioka, Osaka Prefecture University
Sigeru Omatu, Osaka Prefecture University
In recent years, growing multimedia systems require more efficient signal separation methods to preserve the quality of voice or music recording under a noisy environment. Some of signal separation methods are based on minimizing the dependent measure among input signals to separate a noise component since a noise component is usually independent on 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 (GA). In this paper, we have proposed an improved method using differential information. As the result of the simulation, the separated signals are clearly separated to original signals.
Citation:
Michifumi Yoshioka, Sigeru Omatu, "Independent Component Analysis Using Time Delayed Sampling," ijcnn, vol. 4, pp.4075, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000
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