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 Träumerei and Debussy's Syrinx.

[1]  Nicholas Cook,et al.  A guide to musical analysis , 1987 .

[2]  P. Arabie,et al.  Overlapping Clustering: A New Method for Product Positioning , 1981 .

[3]  Dominik Hörnel,et al.  Investigating the Influence of Representations and Algorithms in Music Classification , 2001, Comput. Humanit..

[4]  B. Repp Diversity and commonality in music performance: an analysis of timing microstructure in Schumann's "Träumerei". , 1992, The Journal of the Acoustical Society of America.

[5]  E M I L I O S C A M B O U R O P O U L,et al.  Melodic Cue Abstraction , Similarity , and Category Formation : A Formal Model , 2002 .

[6]  Gert Westermann,et al.  Classification in Music: A Computational Model for Paradigmatic Analysis , 1997, ICMC.

[7]  Costas S. Iliopoulos,et al.  Pattern Processing in Melodic Sequences: Challenges, Caveats and Prospects , 2001, Comput. Humanit..

[8]  P. Arabie Clustering representations of group overlap , 1977 .

[9]  Alan Smaill,et al.  Similarity and Categorisation Inextricably Bound Together , 1997 .

[10]  Morelli Giovanni,et al.  Recensione a: New Grove Dictionary of Music and Musicians, ed. S.Sadie, I-XX, London, Macmillan 1980 , 1985 .

[11]  Gerhard Widmer,et al.  A clustering algorithm for melodic analysis , 1999 .

[12]  Gerhard Widmer,et al.  Large-scale Induction of Expressive Performance Rules: First Quantitative Results , 2000, ICMC.

[13]  Jean-Gabriel Ganascia,et al.  Musical Pattern Extraction and Similarity Assessment , 2000, Readings in Music and Artificial Intelligence.

[14]  J. Nattiez Music and Discourse: Toward a Semiology of Music , 1991 .

[15]  Jean-Jacques Nattiez,et al.  Fondements d'une semiologie de la musique , 1979 .

[16]  Gerhard Widmer,et al.  Learning expressive performance: The structure‐level approach , 1996 .

[17]  Mark A. Gluck,et al.  Information, Uncertainty and the Utility of Categories , 1985 .

[18]  R. Michalski,et al.  Learning from Observation: Conceptual Clustering , 1983 .