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

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Χονδροδήμα, Ευαγγελία	el
dc.contributor.author	Παϊβανά, Γεωργία	el
dc.contributor.author	Στογιάννος, Μάριος	el
dc.contributor.author	Ζώης, Ηλίας Ν.	el
dc.date.accessioned	2015-06-03T18:35:30Z
dc.date.issued	2015-06-03
dc.identifier.uri	http://hdl.handle.net/11400/15003
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://ieeexplore.ieee.org/	en
dc.subject	Matthews correlation coefficient
dc.subject	music genre classification
dc.subject	neural networks
dc.subject	particle swarm optimization
dc.subject	radial basis function
dc.subject	νευρωνικά δίκτυα
dc.subject	ακτινική συνάρτηση βάσης
dc.subject	ταξινόμηση είδους μουσικής
dc.subject	βελτιστοποίηση σμήνους σωματιδίων
dc.title	Music genre classification using radial basis function networks and particle swarm optimization	en
heal.type	conferenceItem
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.contributorName	Σαρίμβεης, Χαράλαμπος	el
heal.identifier.secondary	DOI: 10.1109/CEEC.2014.6958551
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2014-11-14
heal.bibliographicCitation	Alexandridis, A.P., Chondrodima, E., Paivana, G., Stogiannos, M., Zois, E.N., et al. (2014) Music genre classification using radial basis function networks and particle swarm optimization, In: Proceedings of the 6th Computer Science and Electronic Engineering Conference, CEEC 2014. University of EssexColchester, United Kingdom. 25-26 September, 2014. [online]. p. 35-40, 6958551. Available from: http://ieeexplore.ieee.org/	en
heal.abstract	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 the Matthews correlation coefficient (MCC), which can better reflect the success rate per individual class, by summarizing the entire confusion matrix. The resulting classification scheme is applied to the well-known GTZAN dataset, where the objective is to classify 10 different musical genres, based on half-minute music audio excerpts. A comparison with different classifiers shows that the proposed approach offers improved classification accuracy.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	false
heal.conferenceName	6th Computer Science and Electronic Engineering Conference, CEEC 2014	en
heal.conferenceItemType	poster