Automatic subgenre classification of heavy metal music
DisciplineMusic, Mind and Technology (maisteriohjelma)Master's Degree Programme in Music, Mind and Technology
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. ...
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