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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Additional info for Automatic Subgenre Classification of Heavy Metal Music
Extreme Metal: Music and Culture on the Edge. Berg. Kirss, P. (2007). Audio Based Genre Classification of Electronic Music. Master's Thesis, Universitu of Jyväskylä, Jyväskylä. , & Pampalk, E. (2008). Social Tags and Music Information Retrieval. International Society of Music Information Retrieval (Tutorials Session). Langley, P. (1994). Selection of relevant features in machine learning. Proceedings of the AAAI Fall Symposium on Relevance. AAAI Press. , & Toiviainen, P. (2007). MIR in Matlab: A toolbox for musical feature extraction.
1999) was selected from the filter selection category. CSF is a filter algorithm that tries to find a subset of attributes which are highly correlated with the class but have the lowest possible inter-correlations. This is achieved by the correlation-based evaluation function: ???????? = ????????���� ???????? �???? + ????(???? − 1)????���� ???????? Where ???????? represents heuristic ‘merit’ of a feature subset S containing ???? features, ���� ???????????? is the mean feature-class correlation and ���� ???????????? is the average feature-feature correlation.
Therefore, the optimal subset of attributes was selected that at least had no less predictive power than the initial set and did not include redundant information. This stage is one of the most important in the classification process. Various feature selection methods and corresponding algorithms are available for this purpose. In this thesis search-based feature selection algorithms are employed. The concept of the majority of search-based feature selectors is that the algorithm searches through the feature space to find the subset that most likely will predict the class best.