Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/15003
Title: Music genre classification using radial basis function networks and particle swarm optimization
Authors: Αλεξανδρίδης, Αλέξανδρος Π.
Χονδροδήμα, Ευαγγελία
Παϊβανά, Γεωργία
Στογιάννος, Μάριος
Ζώης, Ηλίας Ν.
Contributors: Σαρίμβεης, Χαράλαμπος
Item type: Conference publication
Conference Item Type: Poster
Keywords: Matthews correlation coefficient;music genre classification;neural networks;particle swarm optimization;radial basis function;νευρωνικά δίκτυα;ακτινική συνάρτηση βάσης;ταξινόμηση είδους μουσικής;βελτιστοποίηση σμήνους σωματιδίων
Subjects: Technology
Electrical engineering
Τεχνολογία
Ηλεκτρολογία Μηχανολογία
Issue Date: 3-Jun-2015
14-Nov-2014
Publisher: IEEE
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.
Language: English
Citation: 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/
Conference: 6th Computer Science and Electronic Engineering Conference, CEEC 2014
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/15003
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