Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/16440
Título: | Using text mining techniques for classical music scores analysis |
Autor(es): | Simões, Alberto Lourenço, Anália Almeida, J. J. |
Palavras-chave: | Music mining Music score mining Document classification |
Data: | Dez-2007 |
Revista: | New Trends in Artificial Intelligence |
Resumo(s): | Music Classification is a particular area of Computational Musicology that provides valuable insights about the evolving of compo- sition patterns and assists in catalogue generation. The proposed work detaches from former works by classifying music based on music score in- formation. Text Mining techniques support music score processing while Classification techniques are used in the construction of decision mod- els. Although research is still at its earliest beginnings, the work already provides valuable contributes to symbolic music representation process- ing and subsequent analysis. Score processing involved the counting of ascending and descending chromatic intervals, note duration and meta- information tagging. Analysis involved feature selection and the evalu- ation of several data mining algorithms, ensuring extensibility towards larger repositories or more complex problems. Experiments report the analysis of composition epochs on a subset of the Mutopia project open archive of classical LilyPond-annotated music scores. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/16440 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | DI/CCTC - Artigos (papers) |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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epia-music-2007.pdf | Documento principal | 147,73 kB | Adobe PDF | Ver/Abrir |