Using Text Mining Techniques for Classical Music Scores Analysis

Music Classification is a particular area of Computational Musicology that provides valuable insights about the evolving of composition patterns and assists in catalogue generation. The proposed work detaches from former works by classifying music based on music score information. Text Mining techniques support music score processing while Classification techniques are used in the construction of decision models. Although research is still at its earliest beginnings, the work already provides valuable contributes to symbolic music representation processing and subsequent analysis. Score processing involved the counting of ascending and descending chromatic intervals, note duration and metainformation tagging. Analysis involved feature selection and the evaluation 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.