Abstract
This paper describes a data model for the representation of tonal music. In this model, music is conceived as an equally-spaced time series of 12-dimensional vectors. The model has been successfully applied to the task of discovering frequently recurring patterns, and to the related task of retrieving user-defined musical patterns. This was accomplished by converting midi sequences of music by W.A. Mozart into the time series representation and analyzing these with data mining tools and SQL queries. The novelty of the pattern extraction capability supported by the model is in the potentially complex description of the sequences, which may contain both melodic and harmonic features, may be embedded within each other, or interspersed with other patterns or occurrences. A unique feature of the model is the use of time intervals as the basic representational unit, which fosters possibilities for future application to audio data.
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References
Rolland, P.Y., Ganascia, J.G.: Musical pattern extraction and similarity assessment. In: Miranda, E.R. (ed.) Readings in music and artificial intelligence (2000)
Huron, D.: Music Research Using Humdrum, Center for Computer Assisted Research in the Humanities, Ohio State University, Columbus, Ohio (1999)
Meek, C., Birmingham, W.P.: Thematic Extractor. In: ISMIR 2001, Bloomington, Indiana (2001)
Downie, J.S., Nelson, M.: Evaluation of a Simple and Effective Music Information Retrieval Method. In: 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Athens, Greece (2000)
Doraisamy, S., Ruger, S.: Robust Polyphonic Music Retrieval with N-grams. Journal of Intelligent Information Systems 21(1), 53–70 (2003)
Birmingham, W.P., Dannenberg, R.B., Wakefield, G.H., Bartsch, M., Bykowski, D., Mazzoni, D., Meek, C., Mellody, M., Rand, W.: Musart: Music Retrieval Via Aural Queries. In: ISMIR 2001, Bloomington, Indiana (2001)
McNab, R.J., Smith, L.A., Witten, I.H., Henderson, C.L., Cunningham, S.J.: Towards the Digital Music Library: Tune Retrieval from Acoustic Input. Digital Libraries (1996)
Rumelhart, D.E.: Schemata: The Building Blocks of Cognition. In: Spiro, R.J., Bruce, B.C., Brewer, W.F. (eds.) Theoretical Issues in Reading Comprehension. Erlbaum, Mahwah (1980)
Meyer, L.B.: Explaining Music: Essays and Explorations, p. 213. The University of Chicago Press, Chicago (1973)
Gjerdingen, R.O.: A Classic Turn of Phrase: Music and the Psychology of Convention. University of Pennsylvania Press, Philadelphia (1988)
Gjerdingen, R.O.: Courtly Behavior. Music Perception 13 (1996)
Classical Music Archives, http://www.classicalarchives.com
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan (1995)
SAS version 9.1
Downie, J.S., Futrelle, J., Tcheng, D.: The International Music Information Retrieval Systems Evaluation Laboratory: Governance, Access, and Security. In: ISMIR 2004, Barcelona, Spain (2004)
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Berman, T. (2006). A Data Model for Music Information Retrieval. In: Etzion, O., Kuflik, T., Motro, A. (eds) Next Generation Information Technologies and Systems. NGITS 2006. Lecture Notes in Computer Science, vol 4032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780991_15
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DOI: https://doi.org/10.1007/11780991_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35472-7
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