Evaluating melodic segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2445, Issue , 2002, Pages 168-182

  Spevak, Christian ^a   Thom, Belinda ^a   Höthker, Karin ^a  

^a UNIVERSITY OF KARLSRUHE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTERS;

COHERENT SOLUTIONS; PROBABILISTIC FRAMEWORK; SEGMENTATION ALGORITHMS;

ARTIFICIAL INTELLIGENCE;

EID: 84937697797     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45722-4_16     Document Type: Conference Paper

References (12)

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3. R. Bod (2001). Memory-based models of melodic analysis: challenging the gestalt principles. Journal of New Music Research, 30(3):In Press.
4. E. Cambouropoulos (1998). Musical Parallelism and Melodic Segmentation. In Proceedings of the XII Colloquium on Musical Informatics, Gorizia, Italy, pages 111-114.
5. E. Cambouropoulos (2001). The Local Boundary Detection Model (LBDM) and its application in the study of expressive timing. In: Proceedings of the International Computer Music Conference (ICMC), Havana, Cuba, pages 290-293.
6. T. Crawford, C. S. Iliopoulos, and R. Raman (1998). String-matching techniques for musical similarity and melodic recognition. In: W. B. Hewlett and E. Selfridge- Field, editors, Melodic Similarity: Concepts, Procedures, and Applications, pages 73-100. MIT Press.
7. E. Dahlig (2002). Personal communication.
8. K. Höthker, B. Thom, and C. Spevak (2002). Melodic segmentation: evaluating the performance of algorithms and musicians. Technical Report 2002-3, Faculty of Computer Science, University of Karlsruhe.
9. F. Lerdahl and R. Jackendoff(1983). A Generative Theory of Tonal Music. MIT Press.
10. H. Schaffrath (1995). The Essen Folksong Collection. D. Huron, editor. Center for Computer Assisted Research in the Humanities, Stanford, California.
11. D. Temperley (2001). The Cognition of Basic Musical Structures. MIT Press.
12. B. Thom, C. Spevak, and K. Höthker (2002). Melodic segmentation: evaluating the performance of algorithms and musical experts. Submitted to the International Computer Music Conference.