Music genre classification using radial basis function networks and particle swarm optimization

@article{Alexandridis2014MusicGC,
  title={Music genre classification using radial basis function networks and particle swarm optimization},
  author={Alex Alexandridis and Eva Chondrodima and Georgia Paivana and Marios Stogiannos and Elias Zois and Haralambos Sarimveis},
  journal={2014 6th Computer Science and Electronic Engineering Conference (CEEC)},
  year={2014},
  pages={35-40}
}
This work presents the development of an intelligent system able to classify different music genres with increased accuracy. The proposed approach is based on radial basis function (RBF) networks, trained with the non-symmetric fuzzy means particle swarm optimization-based (PSO-NSFM) algorithm. PSO-NSFM, which has been shown to produce highly accurate regression models, is in this case suitably tailored to accommodate for classification problems. The classifier's performance is evaluated using… CONTINUE READING
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Neural Networks: A Comprehensive Foundation, 2nd ed

  • S. Haykin
  • Upper Saddle River, NJ: Prentice Hall…
  • 1999
Highly Influential
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