Automatic subgenre classification of heavy metal music

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dc.contributor.author Tsatsishvili, Valeri
dc.date.accessioned 2012-01-19T06:12:14Z
dc.date.available 2012-01-19T06:12:14Z
dc.date.issued 2011
dc.identifier.uri http://hdl.handle.net/123456789/37227
dc.identifier.uri http://urn.fi/URN:NBN:fi:jyu-201201191046 en
dc.description.abstract Automatic genre classification of music has been of interest for researchers over a decade. Many success-ful methods and machine learning algorithms have been developed achieving reasonably good results. This thesis explores automatic sub-genre classification problem of one of the most popular meta-genres, heavy metal. To the best of my knowledge this is the first attempt to study the issue. Besides attempting automatic classification, the thesis investigates sub-genre taxonomy of heavy metal music, highlighting the historical origins and the most prominent musical features of its sub-genres. For classification, an algorithm proposed in (Barbedo & Lopes, 2007) was modified and implemented in MATLAB. The obtained results were compared to other commonly used classifiers such as AdaBoost and K-nearest neighbours. For each classifier two sets of features were employed selected using two strategies: Correlation based feature selection and Wrapper selection. A dataset consisting of 210 tracks representing seven genres was used for testing the classification algorithms. Implemented algorithm classified 37.1% of test samples correctly, which is significantly better performance than random classification (14.3%). However, it was not the best achieved result among the classifiers tested. The best result with correct classification rate of 45.7% was achieved by AdaBoost algorithm.
dc.format.extent 59 s.
dc.language.iso eng
dc.rights This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. en
dc.rights Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. fi
dc.subject.other Automatic genre classification
dc.subject.other heavy metal
dc.subject.other subgenre
dc.title Automatic subgenre classification of heavy metal music
dc.type Book en
dc.identifier.urn URN:NBN:fi:jyu-201201191046
dc.subject.ysa heavy rock
dc.subject.ysa musiikki
dc.subject.ysa genret
dc.subject.ysa luokitukset
dc.type.dcmitype Text en
dc.type.ontasot Pro gradu fi
dc.type.ontasot Master’s thesis en
dc.contributor.tiedekunta Humanistinen tiedekunta fi
dc.contributor.tiedekunta Faculty of Humanities en
dc.contributor.laitos Musiikin laitos fi
dc.contributor.laitos Department of Music en
dc.contributor.yliopisto University of Jyväskylä en
dc.contributor.yliopisto Jyväskylän yliopisto fi
dc.contributor.oppiaine Master's Degree Programme in Music, Mind and Technology en
dc.contributor.oppiaine Music, Mind and Technology (maisteriohjelma) fi
dc.date.updated 2012-01-19T06:12:14Z

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