Automated motivic analysis via melodic clustering

Journal of New Music Research

Volumn 29, Issue 4, 2000, Pages 303-317

  Cambouropoulos, Emilios ^a   Widmer, Gerhard ^a  

^a AUSTRIAN RESEARCH INSTITUTE FOR ARTIFICIAL INTELLIGENCE   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84991566509     PISSN: 09298215     EISSN: 17445027     Source Type: Journal    
DOI: 10.1080/09298210008565464     Document Type: Article

References (22)

1. Anagnostopoulou, C., & Westermann, G., (1997). Classification in music: A computational model for paradigmatic analysis. In Proceedings of the International Computer Music Conference 1997. San Francisco: International Computer Music Association.
2. Arabie, P., 1981. Overlapping clustering: A new method for product positioning. Journal of Marketing Research, 18: 310–317.
3. Arabie, P., 1977. Clustering representations of group overlap. Journal of Mathematical Sociology, 5: 113–128.
4. Bent, I. D., (1980). Analysis. In The New Grove Dictionary of Music and Musicians, Vol. 1. London: Macmillan Press.
5. Bomze, I. M., Budinich, M., Pardalos, P. M., and Pelillo, M., 1999. “ The maximum clique problem ”. In Handbook of Combinatorial Optimisation, Edited by: Du, D.-Z., and Pardalos, P. M., The Netherlands, Dordrecht: Kluwer Academic Publishers.
6. Cambouropoulos, E., (2001). Melodic cue abstraction, similarity and category formation: A formal model. Music Perception, 18 (3). (forthcoming).
7. Cambouropoulos, E., (1998). Musical parallelism and melodic segmentation. In Proceedings of the XII Colloquium of Musical Informatics, Gorizia, Italy.
8. Cambouropoulos, E., Crawford, T., & Iliopoulos, C. S., (2001). Pattern processing in melodic sequences: Challenges, caveats and prospects. Computing and the Humanities, 35 (1), 9–21 (forthcoming).
9. Cambouropoulos, E., Smaill, A., & Widmer, G., (1999). A clustering algorithm for melodic analysis. In Proceedings of the Diderot Forum on Mathematics and Music. Vienna, Austria.
10. Cambouropoulos, E., & Smaill, A., (1997). Similarity and categorisation inextricably bound together: The unscramble machine learning algorithm. In Proceedings of the Interdisciplinary Workshop on Similarity and Categorisation, University of Edinburgh Edinburgh.
11. Cook, N., 1987. A Guide to Musical Analysis, London: J. M. Dent and Sons Ltd.
12. Fisher, D. H., 1987. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2 (2): 139–172.
13. Gluck, M. A., & Corter, J. E., (1985). Information, uncertainty, and the utility of categories. In Proceedings of the Seventh Annual Conference of the Cognitive Science Society Irvine, Ca: Lawrence Erlbaum Associates.
14. Höthker, K., Hörnel, D., and Anagnostopoulou, C., 2001. Investigating the influence of representations and algorithms in music classification. Computing and the Humanities, 35 (1): 65–79.
15. Michalski, R. S., 1983. “ Learning from observation: Conceptual clustering ”. In Machine Learning: An Artificial Intelligence Approach, Edited by: Michalski, R. S., Berlin: Springer-Verlag.
16. Nattiez, J.-J., 1990. Music and discourse: Towards a semiology of music, Princeton: Princeton University Press.
17. Nattiez, J.-J., 1975. Fondements d'une Sémiologie de la Musique, Paris: Union Générale d'Editions.
18. Repp, B., 1992. Diversity and commonality in music performance: An analysis of timing microstructure in Schumann's Träumerei. Journal of the Acoustical Society of America, 92 (5): 2546–2568.
19. Rolland, P. Y., & Ganascia, J. G., (2000). Musical pattern extraction and similarity assessment. In E., Miranda Readings in Music and Artificial Intelligence. London: Harwood Academic Publishers (in press).
20. Stix, V., (2000). Finding all maximal cliques in evolving graphs. Research Report, Department of Statistics, University of Vienna, Austria.
21. Widmer, G., (2000). Large-scale induction of expressive performance rules: First qualitative results. In Proceedings of the International Computer Music Conference 2000. San Francisco: International Computer Music Association.
22. Widmer, G., 1996. Learning expressive performance: The structure-level approach. Journal of New Music Research, 25 (2): 179–205.