Automatic Subgenre Classification of Heavy Metal Music by Valeri Tsatsishvili

By Valeri Tsatsishvili

Автор, основываясь на строго научном подходе, рассматривает вопросы, возникающие при попытке классифицировать жанры метал-музыки и предлагает собственное решение проблемы. Предлагаемый материал - часть его магистерской диссертации.

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18 WEKA is open source software issued under the GNU General public license. It features a wide variety of machine learning algorithms as well as data transformation and visualization tools which can be accessed via a graphical user interface or command line. 2 Dataset Since no study has yet been published on automatic genre classification of metal, it was impossible to find any previously used dataset to test the classifier and directly compare the results. g. the above mentioned GTZAN, ISMIR, and Magnatune 19 datasets) are not suitable for our purposes due to being too general in terms of the employed taxonomy.

3 K-Nearest Neighbours K-NN is one of the simplest classification techniques. It has virtually no learning stage and the classification procedure simply consists of measuring pairwise distances between the input test vector and all training vectors, finding K nearest neighbours to the test vector and choosing the class that the majority of these neighbours belong to. The metric used in this thesis for a distance measurement is Euclidean distance. g. , 2009) In spite of having virtually no learning stage, K-NN is not a computationally efficient classifier because it iteratively measures pairwise distances, which becomes tedious for large datasets.

23 The only study about subgenre classification was a master’s thesis about subgenre classification of electronic music (Kirss, 2007), but due to the musical differences between the genres, it was impossible to use it for solving problems arising in this study. 51 52 AUTOMATIC SUBGENRE CLASSIFICATION OF HEAVY METAL MUSIC 6. FUTURE PROSPECTS I believe that the results of the automatic subgenre classification of metal music achieved in this thesis are not the top limit that cannot be overcome. There is a big room for improvements by fine-tuning the implemented algorithm, better parametrization, and summarisation.

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