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Author  |
Tsatsishvili, Valeri | ||||
| Title | Automatic Subgenre Classification of Heavy Metal Music | Type | Book Whole | ||
| Year | 2011 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 65 | ||
| Keywords | Automatic genre classification; classifications; genre; heavy metal; heavy rock; music; subgenre | ||||
| 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. (Source: https://jyx.jyu.fi/handle/123456789/37227#) |
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| Address | |||||
| Corporate Author | Thesis | Master's thesis | |||
| Publisher | University of Jyväskylä | Place of Publication | Jyväskylä, Finland | Editor | |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | Programme in Music, Mind and Technology | Approved | no | ||
| Call Number | INTech @ brianhickam2019 @ | Serial | 2606 | ||
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