Abstract
This paper presents a representation for melodic segment classes and applies it to music data mining. Melody is modeled as a sequence of segments, each segment being a sequence of notes. These segments are assigned to classes through a knowledge representation scheme which allows the flexible construction of abstract views of the music surface. The representation is applied to sequential pattern discovery and to the statistical modeling of musical style.
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References
Anagnostopoulou, C., & Westermann, G. (1997). Classification in music: A computational model for paradigmatic analysis. In Proceedings of the International Computer Music Conference (pp. 125–128). Thessaloniki.
Assayag, G., & Dubnov, S. (2004). Using factor oracles for machine improvisation. Soft Computing, 8, 604–610.
Balaban, M. (1996). The music structures approach in knowledge representation for music processing. Computer Music Journal, 20(2), 96–111.
Bod, R. (2002). Memory-based models of melodic analysis: Challenging the Gestalt principles. Journal of New Music Research, 31(1), 27–37.
Brachman, R., & Levesque, H. (2004). Knowledge representation and reasoning. Morgan Kaufmann.
Brown, M., & Dempster, D. J. (1989). The scientific image of music theory. Journal of Music Theory, 33(1), 65–106.
Brown, P., Della Pietra, V., deSouza, P., Lai, J., & Mercer, R. (1992). Class-based n-gram models of natural language. Computational Linguistics, 18(4), 467–479.
Cambouropoulos, E. (1998). Towards a general computational theory of musical structure. PhD thesis, Faculty of Music, University of Edinburgh.
Cambouropoulos, E., & Widmer, G. (2000). Motivic analysis via melodic clustering. Journal of New Music Research, 29(4), 347–370.
Conklin, D. (2002). Representation and discovery of vertical patterns in music. In C. Anagnostopoulou, M. Ferrand, and A. Smaill (Eds.), Music and artificial intelligence: Lecture notes in artificial intelligence 2445 (pp. 32–42). Springer-Verlag.
Conklin, D. (2003). Music generation from statistical models. In Proceedings of the AISB Symposium on Artificial Intelligence and Creativity in the Arts and Sciences (pp. 30–35). Aberystwyth.
Conklin, D., & Anagnostopoulou, C. (2001). Representation and discovery of multiple viewpoint patterns. In Proceedings of the International Computer Music Conference (pp. 479–485). Havana.
Conklin, D., & Anagnostopoulou, C. (2006). Segmental pattern discovery in music. INFORMS Journal on Computing, 18(3).
Conklin, D., & Witten, I. (1995). Multiple viewpoint systems for music prediction. Journal of New Music Research, 24(1), 51–73.
Cook, N. (1987). A guide to musical analysis. Oxford University Press.
Cope, D. (1991). Computers and musical style. A-R Editions, Madison, WI.
Creighton, H. (1966). Songs and ballads from nova scotia. New York: Dover Publications, Inc.
Dubnov, S., Assayag, G., Lartillot, O., & Bejerano, G. (2003). Using machine-learning methods for musical style modeling. IEEE Computer, 36(10), 73–80.
Forte, A. (1973). The structure of atonal music. Yale University Press.
Galescu, L., & Allen, J. (2000). Hierarchical statistical language models: Experiments on in-domain adaptation. In Proceedings of the International Conference on Spoken Language Processing (pp. 186–189). Bejing.
Höthker, K., Hörnel, D., & Anagnostopoulou, C. (2001). Investigating the influence of representations and algorithms in music classification. Computers and the Humanities, 35, 65–79.
Hsu, J.-L., Liu, C.-C., & Chen, A. (2001). Discovering nontrivial repeating patterns in music data. IEEE Transactions on Multimedia, 3, 311–325.
Hudak, P., Makucevich, T., Gadde, S., & Whong, B. (1996). Haskore music notation—An algebra of music. Journal of Functional Programming, 6(3), 465–483.
Huron, D. (1996). The melodic arch in Western folksongs. Computing in Musicology, 10, 3–23.
Jurafsky, D., & Martin, J. (2000). Speech and language processing. Englewood Cliffs, NJ: Prentice-Hall.
Lartillot, O. (2004). An adaptive and multi-parametric approach for motivic pattern discovery. In Proceedings of the Sound and Music Computing Conference (pp. 117–124). Paris.
Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. Cambridge, MA: MIT Press.
Marsden, A. (2000). Representing musical time: A temporal logic approach. Swets and Zeitlinger.
Meredith, D., Lemström, K., & Wiggins, G. (2002). Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music. Journal of New Music Research, 31(4), 321–345.
Nattiez, J.-J., (1975). Fondements d’;une Sémiologie de la Musique. Union Générale d’;Editions, Paris.
Ostendorf, M., Digalakis, V., & Kimball, O. (1996). From HMMs to segment models: A unified view of stochastic modeling for speech recognition. IEEE Transactions on Acoustics, Speech And Signal Processing, 4, 360–378.
Pachet, F. (2003). The continuator: Musical interaction with style. Journal of New Music Research, 32(3), 333–341.
Pearce, M. (2005). The construction and evaluation of statistical models of melodic structure in music perception and cognition. PhD thesis, Department of Computing, City University, London.
Pickens, J., & Crawford, T. (2002). Harmonic models for polyphonic music retrieval. In Proceedings of the ACM International Conference on Information and Knowledge Management (pp. 430–437). McLean, VA.
Pienimäki, A., & Lemström, K. (2004). Clustering symbolic music using paradigmatic and surface level analyses. In Proceedings of the Second Annual International Symposium on Music Information Retrieval (pp. 262–265). Barcelona.
Ponce de León, P., & Iñesta, J. (2003). Feature-driven recognition of music styles. In Proceedings of the 1st Iberian Conference on Pattern Recognition and Image Analysis: Lecture Notes in Computer Science, volume 2652 (pp. 773–781). Springer-Verlag.
Ries, K., Buø, F., & Waibel, A. (1996). Class phrase models for language modeling. In Proceedings of the International Conference on Spoken Language Processing (pp. 398–401). Philadelphia.
Rolland, P.-Y., & Ganascia, J.-G. (2000). Musical pattern extraction and similarity assessment. In Miranda, E. (Ed), Readings in music and artificial intelligence, chapter 7 (pp. 115–144). Amsterdam: Harwood Academic Publishers.
Temperley, D. (2004). Bayesian models of musical structure and cognition. Musicae Scientiae, 8(2), 175–205.
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Editor: Gerhard Widmer
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Conklin, D. Melodic analysis with segment classes. Mach Learn 65, 349–360 (2006). https://doi.org/10.1007/s10994-006-8712-x
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DOI: https://doi.org/10.1007/s10994-006-8712-x