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dc.contributor.authorTsatsishvili, Valeri
dc.date.accessioned2012-01-19T06:12:14Z
dc.date.available2012-01-19T06:12:14Z
dc.date.issued2011
dc.identifier.otheroai:jykdok.linneanet.fi:1192287
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/37227
dc.description.abstractAutomatic 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.extent59 sivua
dc.language.isoeng
dc.rightsThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.en
dc.rightsJulkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.fi
dc.subject.otherAutomatic genre classification
dc.subject.otherheavy metal
dc.subject.othersubgenre
dc.titleAutomatic subgenre classification of heavy metal music
dc.typeBooken
dc.identifier.urnURN:NBN:fi:jyu-201201191046
dc.type.dcmitypeTexten
dc.type.ontasotPro gradufi
dc.type.ontasotMaster’s thesisen
dc.contributor.tiedekuntaHumanistinen tiedekuntafi
dc.contributor.tiedekuntaFaculty of Humanitiesen
dc.contributor.laitosMusiikin laitosfi
dc.contributor.laitosDepartment of Musicen
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.oppiaineMusic, Mind and Technology (maisteriohjelma)fi
dc.contributor.oppiaineMaster's Degree Programme in Music, Mind and Technologyen
dc.date.updated2012-01-19T06:12:14Z
dc.rights.accesslevelopenAccessfi
dc.contributor.oppiainekoodi3054
dc.subject.ysoheavy rock
dc.subject.ysomusiikki
dc.subject.ysogenret
dc.subject.ysoluokitukset


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