Global feature versus event models for folk song classification

Proceedings of the 10th International Society for Music Information Retrieval Conference, ISMIR 2009

Volumn , Issue , 2009, Pages 729-733

  Hillewaere, Ruben ^a   Manderick, Bernard ^a   Conklin, Darrell ^b  

^a VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

^b CITY UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DEFAULT MODEL; EVENT MODEL; FEATURE VECTORS; FOLK SONGS; GEOGRAPHIC REGIONS; GLOBAL FEATURE; LEARNING APPROACH; MUSIC CLASSIFICATION;

INFORMATION RETRIEVAL;

EID: 84873630448     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. A. Lomax: "Folk Song Style: Notes on a Systematic Approach to the Study of Folk Song, " Journal of the International Folk Music Council, Vol. 8, pp. 48-50, 1956.
2. P. J. Ponce de Léon and J. M. Iñesta: "Statistical description models for melody analysis and characterization, " Proceedings of the 2004 International Computer Music Conference, pp. 149-156, 2004.
3. C. McKay and I. Fujinaga: "Automatic genre classification using large high-levelmusical feature sets, " Proceedings of the International Conference on Music Information Retrieval, pp. 525-530, 2004.
4. A. Volk, P. van Kranenburg, J. Garbers, F. Wiering, R.C. Veltkamp and L.P. Grijp: "A manual annotation method for melodic similarity and the study of melody feature sets, " Proceedings of the 9th International Conference on Music Information Retrieval, pp. 101-106, 2008.
5. D. Conklin and I. H. Witten: "Multiple viewpoint systems for music prediction, " Journal of New Music Research, Vol. 24, No. 1, pp. 51-73, 1995.
6. X. Li, G. Li and J. Bilmes: "A factored languagemodel of quantized pitch and duration, " International Computer Music Conference, 2006.
7. D. Müllensiefen: FANTASTIC: Feature ANalysis Technology Accessing STatistics (In a Corpus): Technical Report v0.9, 2009.
8. http://doc.gold.ac.uk/isms/mmm.
9. W. Steinbeck: Struktur und Ähnlichkeit: Methoden automatisierter Melodieanalyse, Bärenreiter, Kassel, 1982.
10. D. Huron: "The melodic arch in western folksongs, " Computing in Musicology, 10, pp. 3-23, 1996.
11. B. Jesser: Interaktive Melodieanalyse, Peter Lang, Bern, 1991.
12. http://jmir.sourceforge.net/jSymbolic.html.
13. D. Jurafsky and J. Martin: Speech and Language Processing. Prentice-Hall, Englewood Cliffs, NJ, 2000.
14. D. Conklin: "Melodic analysis with segment classes, " Machine Learning, Vol. 65, pp. 349-360, 2006.
15. M. T. Pearce, D. Müllensiefen and G. A.Wiggins: "An information-dynamicmodel of melodic segmentation, " presented at the International Workshop on Machine Learning and Music. University of Helsinki, Finland. July, 2008.
16. http://www.cs.waikato.ac.nz/ml/weka/.
17. I. H. Witten and E. Frank: Data Mining: practical machine learning tools and techniques, 2nd edition, Morgan Kaufmann, 2005.
18. M. A. Hall: Correlation-based Feature Subset Selection for Machine Learning, PhD Thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 1998.
19. M. Li and R. Sleep: "Melody classification using a similarity metric based on Kolmogorov complexity, " Sound and Music Computing Conference, Paris, 2004.
20. C.R. Lin, N.H. Liu, Y.H. Wu and A.L.P. Chen: "Music classification using significant repeating patterns, " Lecture notes in Computer Science, volume 2973, Springer, pp. 506-518, 2004.