Abstract
In this paper we present a stepwise method for the analysis of musical sequences. The starting point is either a MIDI file or the score of a piece of music. The result is a set of likely themes and motifs. The method relies on a pitch intervals representation of music and an event discovery system that extracts significant and repeated patterns from sequences. We report and discuss the results of a preliminary experimentation, and outline future enhancements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Stamatatos, E., Widmer, G.: Automatic identification of music performers with learning ensembles. Artificial Intelligence 165, 37–56 (2005)
Conklin, D.: Representation and discovery of vertical patterns in music. In: Anagnostopoulou, C., Ferrand, M., Smaill, A. (eds.) ICMAI 2002. LNCS (LNAI), vol. 2445, pp. 32–42. Springer, Heidelberg (2002)
Bod, R.: A Unified Model of Structural Organization in Language and Music. Journal of Artificial Intelligence Research 17, 289–308 (2002)
Widmer, G.: Modeling the Rational Basis of Musical Expression. Computer Music Journal 19(2), 76–96 (1995)
Lopez de Mantaras, R., Arcos, J.: AI and Music: From Composition to Expressive Performance. AI Magazine 23, 43–57 (2002)
Lerdahl, F., Jackendoff, R.: A Generative Theory of Tonal Music. MIT Press, Cambridge (1983)
Bent, I. (ed.): Music Analysis in the Nineteenth Century. Cambridge University Press, Cambridge (1994)
Hsu, J.L., Liu, C.C., Chen, A.: Efficient Repeating Pattern Finding in Music Databases. In: Procs. of International Conference on Knowledge and Information Management, pp. 281–288 (1998)
Meredith, D., Lemström, K., Wiggins, G.: Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music. Journal of New Music Research 31(4), 321–345 (2002)
de la Motte, D.: Kontrapunkt. Ein Lese und Arbeitsbuch. Kassel, Bärenreiter (1981)
Botta, M., Galassi, U., Giordana, A.: Learning Complex and Sparse Events in Long Sequences. In: Procs. of the 16th European Conference on Artificial Intelligence, Valencia, Spain, pp. 425–429 (2004)
Galassi, U., Giordana, A., Saitta, L., Botta, M.: Learning Regular Expressions from Noisy Sequences. In: Procs. of 6th International Symposium on Abstraction, Reformulation and Approximation, Edinburgh, Scotland (UK), pp. 92–107 (2005)
Dovey, M.: A Technique for “regular expression” style searching in polyphonic music. In: Procs. of the Internat. Music Information Retrieval Conference (2001)
Conklin, D., Anagnostopoulou, C.: Representation and Discovery of Multiple Viewpoint Patterns. In: Procs. of the 2001 International Computer Music Conference, pp. 479–485 (2001)
Conklin, D., Witten, I.H.: Multiple viewpoint systems for music prediction. Journal of New Music Research 24(1), 51–73 (1995)
Rolland, P.Y.: Discovering Patterns in Musical Sequences. Journal of New Music Research 28(4), 334–350 (1999)
Navarro, G.: A Guided Tour to Approximate String Matching. ACM Computing Surveys 33(1), 31–88 (2001)
Cambouropoulos, E., Crochemore, M., Iliopoulos, C., Mouchard, L., Pinzon, Y.: Algorithms for Computing Approximate Repetitions in Musical Sequences. Intern. J. Computer Math. 79(11), 1135–1148 (2002)
Lartillot, O.: Discovering Musical Patterns through Perceptive Heuristics. In: Procs. of International Symposium on Music Information Retrieval (2003)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE 77(2), 257–286 (1989)
Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis. Cambridge University Press, Cambridge (1998)
Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden markov model: Analysis and applications. Machine Learning 32, 41–62 (1998)
Murphy, K., Paskin, M.: Linear time inference in hierarchical hmms. In: Advances in Neural Information Processing Systems (NIPS-2001), vol. 14 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Radicioni, D.P., Botta, M. (2006). A Methodological Contribution to Music Sequences Analysis. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_47
Download citation
DOI: https://doi.org/10.1007/11875604_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
eBook Packages: Computer ScienceComputer Science (R0)