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Computational Music Theory and Its Applications to Expressive Performance and Composition

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Guide to Computing for Expressive Music Performance

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Abstract

This chapter describes a musical analysis system based on a generative theory of tonal music (GTTM). Music theory provides methodologies for analyzing and transcribing such knowledge, experiences, and skills from a musician’s perspective. Our concern is whether the concepts necessary for musical analysis are sufficiently externalized in musical theory. Given its ability to formalize musical knowledge, GTTM is considered here to be the most promising theory among the many that have been proposed because it captures the aspects of musical phenomena based on the gestalt in the music and follows relatively rigid rules. This chapter also describes music expectation and melody morphing methods that can use the analysis results from the music analysis system. The music expectation method predicts the next notes needed to assist musical novices in playing improvisations. The melody morphing method generates an intermediate melody between two melodies in a systematic order in accordance with a specific numerical measure.

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Notes

  1. 1.

    In this chapter, the word “parameter” is used not only for parameters used to control a system externally but also for internal variables (intermediated variables) that connect submodules.

References

  1. Apple – GarageBand (2012). http://www.apple.com/ilife/garageband/

  2. Todd N (1985) A model of expressive timing in tonal music. Music Percept 3(1):33–58

    Article  Google Scholar 

  3. Widmer G (1983) Understanding and learning musical expression. In: Proceeding of the 1983 international computer music conference (ICMC1983), New York, pp 268–275

    Google Scholar 

  4. Hirata K, Hiraga R (2003) Ha-Hi-Hun plays Chopin’s Etude. In: Working notes of IJCAI-03 workshop on methods for automatic music performance and their applications in a public rendering contest, Acapulco, pp 72–73

    Google Scholar 

  5. Hirata K, Matsuda S (2003) Interactive music summarization based on generative theory of tonal music. J New Music Res (JNMR) 32(2):165–177

    Article  Google Scholar 

  6. Hamanaka M, Hirata K, Tojo T (2007) Implementing “a generating theory of tonal music”. J New Music Res (JNMR) 35(4):249–277

    Article  Google Scholar 

  7. Hamanaka M, Hirata K, Tojo S (2005) ATTA: automatic time-span tree analyzer based on extended GTTM. In: Proceedings of the 6th international conference on music information retrieval conference (ISMIR2005), London, pp 358–365

    Google Scholar 

  8. Hamanaka M, Hirata K, Tojo S (2007) FATTA: full automatic time-span tree analyzer. In: Proceedings of the 2007 international computer music conference (ICMC2007), Copenhagen, vol 1, pp 153–156

    Google Scholar 

  9. Lerdahl F, Jackendoff R (1983) A generative theory of tonal music. MIT Press, Cambridge, MA

    Google Scholar 

  10. Balaban M (1996) The music structures approach to knowledge representation for music processing. Comput Music J 30(2):96–111

    Article  Google Scholar 

  11. Cope D (1996) Experiments in musical intelligence. A-R Editions, Inc., Madison

    Google Scholar 

  12. Dannenberg R (1997) Machine tongues XIX: Nyquist, a language for composition and sound synthesis. Comput Music J 21(3):50–60

    Article  Google Scholar 

  13. Hirata K, Aoyagi T (2003) Computational music representation based on the generative theory of tonal music and the deductive object-oriented database. Comput Music J 27(3):73–89

    Article  Google Scholar 

  14. Hamanaka M, Hirata K, Tojo S (2009) Melody extrapolation in GTTM approach. In:Proceedings of the 2009 international computer music conference (ICMC2009), Montreal, pp 89–92

    Google Scholar 

  15. Hamanaka M, Hirata K, Tojo S (2008) Melody morphing method based on GTTM. In:Proceedings of the 2008 international computer music conference (ICMC2008), Belfast, pp 155–158

    Google Scholar 

  16. Mozer M (1994) Neural network music composition by prediction: exploring the benefits of psychoacoustic constraints and multi-scale processing. Connect Sci 6(2–3):247–280

    Article  Google Scholar 

  17. ManYat Lo, Simon M. Lucas S (2006) Evolving musical sequences with N-Gram based trainable fitness functions. In: Proceedings of the 2006 IEEE Congress on evolutionary computation, Vancouver, pp 604–614

    Google Scholar 

  18. Cooper G, Meyer LB (1960) The rhythmic structure of music. The University of Chicago Press, London

    Google Scholar 

  19. Narmour E (1990) The analysis and cognition of basic melodic structure. The University of Chicago Press, Chicago

    Google Scholar 

  20. Temperley D (2001) The cognition of basic musical structures. MIT Press, Cambridge

    Google Scholar 

  21. Larson S (2004) Musical forces and melodic expectations: comparing computer models with experimental results. Music Percept 21/4:457–498

    Article  Google Scholar 

  22. Lerdahl F (2001) Tonal pitch space. Oxford University Press, New York

    Google Scholar 

  23. Hamanaka M, Hirata K, Tojo S (2004) Automatic generation of grouping structure based on the GTTM. In: Proceeding of 2004 international computer music conference (ICMC2004), Miami, pp 141–144

    Google Scholar 

  24. Hamanaka M, Hirata K, Tojo S (2005) Automatic generation of metrical structure based on the GTTM. In: Proceeding of 2005 international computer music conference (ICMC2005), Barcelona, pp 53–56

    Google Scholar 

  25. Sakamoto S, Tojo S (2009) Harmony analysis of music in tonal pitch space. Information Processing Society of Japan SIG technical report, vol 2009 (in Japanese)

    Google Scholar 

  26. Hamanaka M, Goto M, Asoh H, Otsu N (2003) A learning-based quantization: unsupervised estimation of the model parameters. In: Proceedings of 2003 international computer music conference (ICMC2003), Singapore, pp 369–372

    Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tenodai, Tsukuba, 305-8577, Japan

    Masatoshi Hamanaka

  2. Faculty of Systems Information Science, Future University Hakodate, 116-2 Kamedanakano-cho, Hakodate, Hokkaido, 041-8655, Japan

    Keiji Hirata

  3. School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, Japan

    Satoshi Tojo

Authors
  1. Masatoshi Hamanaka
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  2. Keiji Hirata
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  3. Satoshi Tojo
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Corresponding author

Correspondence to Masatoshi Hamanaka .

Editor information

Editors and Affiliations

  1. ICCMR, University of Plymouth, Smeaton Building 206, Plymouth, PL4 8AA, Devon, United Kingdom

    Alexis Kirke

  2. Computing, Communication & Electronics, University of Plymouth, Plymouth, PL4 8AA, United Kingdom

    Eduardo R. Miranda

Questions

Questions

  1. 1.

    Why are the systems in this chapter potentially so important to automated systems for expressive music performance?

  2. 2.

    What musicological system are ATTA and FATTA designed to automate?

  3. 3.

    What is prolongation reduction?

  4. 4.

    What is a time-span tree?

  5. 5.

    What do parameterization and externalization partially deal with, thus allowing an analysis to be automated on a computer?

  6. 6.

    When is TSRPR5 applied and what does it result in?

  7. 7.

    What are the meet and join operations?

  8. 8.

    How are meet and join used to morph melodies?

  9. 9.

    Give one method for evaluating melody morphing.

  10. 10.

    How could the melody morphing method be used to smoothly change the ShakeGuitar backing in real time from soft backing to heavy soloing?

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© 2013 Springer-Verlag London

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Hamanaka, M., Hirata, K., Tojo, S. (2013). Computational Music Theory and Its Applications to Expressive Performance and Composition. In: Kirke, A., Miranda, E. (eds) Guide to Computing for Expressive Music Performance. Springer, London. https://doi.org/10.1007/978-1-4471-4123-5_8

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  • DOI: https://doi.org/10.1007/978-1-4471-4123-5_8

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4122-8

  • Online ISBN: 978-1-4471-4123-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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