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OFAI-TR-99-24 ( 31kB g-zipped PostScript file,  19kB PDF file)

A Clustering Algorithm for Melodic Analysis

Emilios Cambouropoulos, Alan Smaill, Gerhard Widmer

In this paper a formal model will be presented that attempts to organise melodic segments into 'significant' musical categories (e.g. motives). Given a segmentation of a melodic surface, the proposed model constructs an appropriate representation for each segment in terms of a number of attributes (these reflect melodic and rhythmic aspects of the segment at the surface and at various abstract levels) and then a clustering algorithm (the Unscramble algorithm) is applied for the organisation of these segments into 'meaningful' categories. The proposed clustering algorithm automatically determines an appropriate number of clusters and also the characteristic (or defining) attributes of each category. As a test case this computational model has been used for obtaining a motivic analysis of three melodies from diverse musical styles.

Keywords: Music, Melodic Similarity, Clustering, Categorisation

Citation: Cambouropoulos E., Smaill A., Widmer G.: A Clustering Algorithm for Melodic Analysis. In Proceedings of the Diderot'99 Forum on Mathematics and Music, 1-4 December 1999, Vienna.