Automated motivic analysis via melodic clustering

In this paper a computational 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 algorithm 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 attributes of each category. As a test case this computational model has been used for obtaining a motivic analysis of Schumann's Traumerei and Debussy's Syrinx.