An Overview of Computer Systems for Expressive Music Performance

Chapter

Abstract

This chapter is a survey of research into automated and semi-automated computer systems for expressive performance of music. We examine the motivation for such systems and then examine a significant sample of the systems developed over the last 30 years. To highlight some of the possible future directions for new research, this chapter uses primary terms of reference based on four elements: testing status, expressive representation, polyphonic ability and performance creativity.

Notes

Acknowledgements

This work was financially supported by the EPSRC-funded project ‘Learning the Structure of Music’, grant EP/D062934/1. An earlier version of this chapter was published in ACM Computing Surveys Vol. 42, No. 1.

References

  1. 1.
    Hiller L, Isaacson L (1959) Experimental music. Composition with an electronic computer. McGraw-Hill, New YorkGoogle Scholar
  2. 2.
    Buxton WAS (1977) A composer’s introduction to computer music. Interface 6:57–72Google Scholar
  3. 3.
    Roads C (1996) The computer music tutorial. MIT Press, CambridgeGoogle Scholar
  4. 4.
    Miranda ER (2001) Composing music with computers. Focal Press, OxfordGoogle Scholar
  5. 5.
    Todd NP (1985) A model of expressive timing in tonal music. Music Percept 3:33–58CrossRefGoogle Scholar
  6. 6.
    Friberg A, Sundberg J (1999) Does music performance allude to locomotion? A model of final ritardandi derived from measurements of stopping runners. J Acoust Soc Am 105:1469–1484CrossRefGoogle Scholar
  7. 7.
    Pace I (2007) Complexity as imaginative stimulant: issues of rubato, barring, grouping, accentuation and articulation in contemporary music, with examples from Boulez, Carter, Feldman, Kagel, Sciarrino, Finnissy. In: Proceedings of the 5th international Orpheus academy for music, theory, Gent, Belgium, Apr 2007Google Scholar
  8. 8.
    Seashore CE (1938) Psychology of music. McGraw-Hill, New YorkGoogle Scholar
  9. 9.
    Palmer C (1997) Music performance. Annu Rev Psychol 48:115–138CrossRefGoogle Scholar
  10. 10.
    Gabrielsson A (2003) Music performance research at the millennium. Psychol Music 31:221–272CrossRefGoogle Scholar
  11. 11.
    Clarke EF (1998) Generative principles in music performance. In: Sloboda JA (ed) Generative processes in music: the psychology of performance, improvisation, and composition. Clarendon, Oxford, pp 1–26Google Scholar
  12. 12.
    Gabrielsson A, Juslin P (1996) Emotional expression in music performance: between the performer’s intention and the listener’s experience. Psychol Music 24:68–91CrossRefGoogle Scholar
  13. 13.
    Juslin P (2003) Five facets of musical expression: a psychologist’s perspective on music performance. Psychol Music 31:273–302CrossRefGoogle Scholar
  14. 14.
    Good M (2001) MusicXML for notation and analysis. In: Hewlett WB, Selfridge-Field E (eds) The virtual score: representation, retrieval, restoration. MIT Press, Cambridge, pp 113–124Google Scholar
  15. 15.
    Lerdahl F, Jackendoff R (1938) A generative theory of tonal music. The MIT Press, CambridgeGoogle Scholar
  16. 16.
    Narmour E (1990) The analysis and cognition of basic melodic structures: the implication-realization model. The University of Chicago Press, ChicagoGoogle Scholar
  17. 17.
    Friberg A, Bresin R, Sundberg J (2006) Overview of the KTH rule system for musical performance. Adv Cognit Psychol 2:145–161CrossRefGoogle Scholar
  18. 18.
    Todd NP (1989) A computational model of Rubato. Contemp Music Rev 3:69–88MathSciNetCrossRefGoogle Scholar
  19. 19.
    Todd NP (1992) The dynamics of dynamics: a model of musical expression. J Acoust Soc Am 91:3540–3550CrossRefGoogle Scholar
  20. 20.
    Todd NP (1995) The kinematics of musical expression. J Acoust Soc Am 97:1940–1949CrossRefGoogle Scholar
  21. 21.
    Clynes M (1986) Generative principles of musical thought: integration of microstructure with structure. Commun Cognit 3:185–223Google Scholar
  22. 22.
    Clynes M (1995) Microstructural musical linguistics: composer’s pulses are liked best by the musicians. Cognit: Int J Cognit Sci 55:269–310CrossRefGoogle Scholar
  23. 23.
    Johnson ML (1991) Toward an expert system for expressive musical performance. Computer 24:30–34CrossRefGoogle Scholar
  24. 24.
    Dannenberg RB, Derenyi I (1998) Combining instrument and performance models for high-quality music synthesis. J New Music Res 27:211–238CrossRefGoogle Scholar
  25. 25.
    Dannenberg RB, Pellerin H, Derenyi I (1998) A study of trumpet envelopes. In: Proceedings of the 1998 international computer music conference, Ann Arbor, Michigan, October 1998. International Computer Music Association, San Francisco, pp 57–61Google Scholar
  26. 26.
    Mazzola G, Zahorka O (1994) Tempo curves revisited: hierarchies of performance fields. Comput Music J 18(1):40–52CrossRefGoogle Scholar
  27. 27.
    Mazzola G (2002) The topos of music – geometric logic of concepts, theory, and performance. Birkhäuser, Basel/BostonMATHGoogle Scholar
  28. 28.
    Hashida M, Nagata N, Katayose H (2006) Pop-E: a performance rendering system for the ensemble music that considered group expression. In: Baroni M, Addessi R, Caterina R, Costa M (eds) Proceedings of 9th international conference on music perception and cognition, Bologna, Spain, August 2006. ICMPC, pp 526–534Google Scholar
  29. 29.
    Sethares W (2004) Tuning, timbre, spectrum, scale. Springer, LondonGoogle Scholar
  30. 30.
    Finn B (2007) Personal communicationGoogle Scholar
  31. 31.
    Livingstone SR, Muhlberger R, Brown AR, Loch A (2007) Controlling musical emotionality: an affective computational architecture for influencing musical emotions. Digit Creat 18:43–53CrossRefGoogle Scholar
  32. 32.
    Katayose H, Fukuoka T, Takami K, Inokuchi S (1990) Expression extraction in virtuoso music performances. In: Proceedings of the 10th international conference on pattern recognition, Atlantic City, New Jersey, USA, June 1990. IEEE Press, Los Alamitos, pp 780–784Google Scholar
  33. 33.
    Aono Y, Katayose H, Inokuchi S (1997) Extraction of expression parameters with multiple regression analysis. J Inf Process Soc Jpn 38:1473–1481Google Scholar
  34. 34.
    Ishikawa O, Aono Y, Katayose H, Inokuchi S (2000) Extraction of musical performance rule using a modified algorithm of multiple regression analysis. In: Proceedings of the international computer music conference, Berlin, Germany, August 2000. International Computer Music Association, San Francisco, pp 348–351Google Scholar
  35. 35.
    Canazza S, Drioli C, De Poli G, Roda A, Vidolin A (2000) Audio morphing different expressive intentions for multimedia systems. IEEE Multimed 7:79–83Google Scholar
  36. 36.
    Canazza S, De Poli G, Drioli C, Roda A, Vidolin A (2001) Expressive morphing for interactive performance of musical scores. In: Proceedings of first international conference on WEB delivering of music, Florence, Italy, Nov 2001. IEEE, Los Alamitos, pp 116–122Google Scholar
  37. 37.
    Canazza S, De Poli G, Roda A, Vidolin A (2003) An abstract control space for communication of sensory expressive intentions in music performance. J New Music Res 32:281–294CrossRefGoogle Scholar
  38. 38.
    Bresin R (1998) Artificial neural networks based models for automatic performance of musical scores. J New Music Res 27:239–270CrossRefGoogle Scholar
  39. 39.
    Camurri A, Dillon R, Saron A (2000) An experiment on analysis and synthesis of musical expressivity. In: Proceedings of 13th colloquium on musical informatics, L’Aquila, Italy, Sept 2000Google Scholar
  40. 40.
    Arcos JL, De Mantaras RL, Serra X (1997) SaxEx: a case-based reasoning system for generating expressive musical performances. In: Cook PR (eds) Proceedings of 1997 international computer music conference, Thessalonikia, Greece, Sept 1997. ICMA, San Francisco, pp 329–336Google Scholar
  41. 41.
    Arcos JL, Lopez De Mantaras R, Serra X (1998) Saxex: a case-based reasoning system for generating expressive musical performance. J New Music Res 27:194–210CrossRefGoogle Scholar
  42. 42.
    Arcos JL, Lopez De Mantaras R (2001) An interactive case-based reasoning approach for generating expressive music. J Appl Intell 14:115–129MATHCrossRefGoogle Scholar
  43. 43.
    Suzuki T, Tokunaga T, Tanaka H (1999) A case based approach to the generation of musical expression. In: Proceedings of the 16th international joint conference on artificial intelligence, Stockholm, Sweden, Aug 1999. Morgan Kaufmann, San Francisco, pp 642–648Google Scholar
  44. 44.
    Suzuki T (2003) Kagurame phase-II. In: Gottlob G, Walsh T (eds) Proceedings of 2003 international joint conference on artificial intelligence (Working Notes of RenCon Workshop), Acapulco, Mexico, Aug 2003. Morgan Kauffman, Los AltosGoogle Scholar
  45. 45.
    Hirata K, Hiraga R (2002) Ha-Hi-Hun: performance rendering system of high controllability. In: Proceedings of the ICAD 2002 RenCon workshop on performance rendering systems, Kyoto, Japan, July 2002, pp 40–46Google Scholar
  46. 46.
    Widmer G (2000) Large-scale induction of expressive performance rules: first quantitative results. In: Zannos I (eds) Proceedings of the 2000 international computer music conference, Berlin, Germany, Sept 2000. International Computer Music Association, San Francisco, 344–347Google Scholar
  47. 47.
    Widmer G (2002) Machine discoveries: a few simple, robust local expression principles. J New Music Res 31:37–50CrossRefGoogle Scholar
  48. 48.
    Widmer G (2003) Discovering simple rules in complex data: a meta-learning algorithm and some surprising musical discoveries. Artif Intell 146:129–148MathSciNetMATHCrossRefGoogle Scholar
  49. 49.
    Widmer G, Tobudic A (2003) Playing Mozart by analogy: learning multi-level timing and dynamics strategies. J New Music Res 32:259–268CrossRefGoogle Scholar
  50. 50.
    Tobudic A, Widmer G (2003) Relational ibl in music with a new structural similarity measure. In: Horvath T, Yamamoto A (eds) Proceedings of the 13th international conference on inductive logic programming, Szeged, Hungary, Sept 2003. Springer Verlag, Berlin, pp 365–382Google Scholar
  51. 51.
    Tobudic A, Widmer G (2003) Learning to play Mozart: recent improvements. In: Hirata K (eds) Proceedings of the IJCAI’03 workshop on methods for automatic music performance and their applications in a public rendering contest (RenCon), Acapulco, Mexico, Aug 2003Google Scholar
  52. 52.
    Raphael C (2001) Can the computer learn to play music expressively? In: Jaakkola T, Richardson T (eds) Proceedings of eighth international workshop on artificial intelligence and statistics, 2001. Morgan Kaufmann, San Francisco, pp 113–120Google Scholar
  53. 53.
    Raphael C (2001) A Bayesian network for real-time musical accompaniment. Neural Inf Process Sys 14:1433–1440Google Scholar
  54. 54.
    Raphael C (2003) Orchestra in a box: a system for real-time musical accompaniment. In: Gottlob G, Walsh T (eds) Proceedings of 2003 international joint conference on artificial intelligence (Working Notes of RenCon Workshop), Acapulco, Mexico, Aug 2003. Morgan Kaufmann, San Francisco, pp 5–10Google Scholar
  55. 55.
    Grindlay GC (2005) Modelling expressive musical performance with Hidden Markov Models. PhD thesis, University of Santa Cruz, CAGoogle Scholar
  56. 56.
    Carlson L, Nordmark A Wikilander R (2003) Reason version 2.5 – getting started. Propellerhead SoftwareGoogle Scholar
  57. 57.
    Dorard L, Hardoon DR, Shawe-Taylor J (2007) Can style be learned? A machine learning approach towards ‘performing’ as famous pianists. In: Music, brain and cognition workshop, NIPS 2007, Whistler, CanadaGoogle Scholar
  58. 58.
    Hazan A, Ramirez R (2006) Modelling expressive performance using consistent evolutionary regression trees. In: Brewka G, Coradeschi S, Perini A, Traverso P (eds) Proceedings of 17th European conference on artificial intelligence (Workshop on Evolutionary Computation), Riva del Garda, Italy, Aug 2006. IOS Press, Washington, DCGoogle Scholar
  59. 59.
    Ramirez R, Hazan A (2007) Inducing a generative expressive performance model using a sequential-covering genetic algorithm. In: Proceedings of 2007 genetic and evolutionary computation conference, London, UK, July 2007. ACM Press, New YorkGoogle Scholar
  60. 60.
    Miranda ER, Kirke A, Zhang Q (2010) Artificial evolution of expressive performance of music: an imitative multi-agent systems approach. Comput Music J 34(1):80–96CrossRefGoogle Scholar
  61. 61.
    Dahlstedt P (2007) Autonomous evolution of complete piano pieces and performances. In: Proceedings of ECAL 2007 workshop on music and artificial life (MusicAL 2007), Lisbon, Portugal, Sept 2007Google Scholar
  62. 62.
    Papadopoulos G, Wiggins GA (1999) AI methods for algorithmic composition: a survey, a critical view, and future prospects. In: Proceedings of the AISB’99 symposium on musical creativity. AISB, EdinburghGoogle Scholar
  63. 63.
    Hiraga R, Bresin R, Hirata K, RenCon KH (2004) Turing test for musical expression proceedings of international conference on new interfaces for musical expression. In: Nagashima Y, Lyons M (eds) Proceedings of 2004 new interfaces for musical expression conference, Hamatsu, Japan, June 2004. Shizuoka University of Art and Culture, ACM Press, New York pp 120–123Google Scholar
  64. 64.
    Arcos JL, De Mantaras RL (2001) The SaxEx system for expressive music synthesis: a progress report. In: Lomeli C, Loureiro R (eds) Proceedings of the workshop on current research directions in computer music, Barcelona, Spain, Nov 2001. Pompeu Fabra University, Barcelona, pp 17–22Google Scholar
  65. 65.
    Church M (2004) The mystery of Glenn Gould. Independent Newspaper, Published by Independent Print Ltd, London, UKGoogle Scholar
  66. 66.
    Kirke A, Miranda ER (2007) Capturing the aesthetic: radial mappings for cellular automata music. J ITC Sangeet Res Acad 21:15–23Google Scholar
  67. 67.
    Anders T (2007) Composing music by composing rules: design and usage of a generic music constraint system. PhD thesis, University of BelfastGoogle Scholar
  68. 68.
    Tobudic A, Widmer G (2006) Relational IBL in classical music. Mach Learn 64:5–24CrossRefGoogle Scholar
  69. 69.
    Sundberg J, Askenfelt A, Fryden L (1983) Musical performance. A synthesis-by-rule approach. Comput Music J 7:37–43CrossRefGoogle Scholar
  70. 70.
    Bresin R, Friberg A (2000) Emotional coloring of computer-controlled music performances. Comput Music J 24:44–63CrossRefGoogle Scholar
  71. 71.
    Friberg A (2006) pDM: an expressive sequencer with real-time control of the KTH music-performance rules. Comput Music J 30:37–48CrossRefGoogle Scholar
  72. 72.
    Desain P, Honing H (1993) Tempo curves considered harmful. Contemp Music Rev 7:123–138CrossRefGoogle Scholar
  73. 73.
    Thompson WF (1989) Composer-specific aspects of musical performance: an evaluation of Clynes’s theory of pulse for performances of Mozart and Beethoven. Music Percept 7:15–42CrossRefGoogle Scholar
  74. 74.
    Repp BH (1990) Composer’s pulses: science or art. Music Percept 7:423–434CrossRefGoogle Scholar
  75. 75.
    Hashida M, Nagata N, Katayose H (2007) jPop-E: an assistant system for performance rendering of ensemble music. In: Crawford L (eds) Proceedings of 2007 conference on new interfaces for musical expression (NIME07), New York, NY, pp 313–316Google Scholar
  76. 76.
    Meyer LB (1957) Meaning in music and information theory. J Aesthet Art Crit 15:412–424CrossRefGoogle Scholar
  77. 77.
    Canazza S, De Poli G, Drioli C, Roda A, Vidolin A (2004) Modeling and control of expressiveness in music performance. Proc IEEE 92:686–701CrossRefGoogle Scholar
  78. 78.
    De Poli G (2004) Methodologies for expressiveness modeling of and for music performance. J New Music Res 33:189–202CrossRefGoogle Scholar
  79. 79.
    Lopez De Mantaras R, Arcos JL (2002) AI and music: from composition to expressive performances. AI Mag 23:43–57Google Scholar
  80. 80.
    Mitchell T (1997) Machine learning. McGraw-Hill, New YorkMATHGoogle Scholar
  81. 81.
    Emde W, Wettschereck D (1996) Relational instance based learning. In: Saitta L (eds) Proceedings of 13th international conference on machine learning, Bari, Italy, July 1996. Morgan Kaufmann, San Francisco, pp 122–130Google Scholar
  82. 82.
    Wright M, Berdahl E (2006) Towards machine learning of expressive microtiming in Brazilian drumming. In: Zannos I (eds) Proceedings of the 2006 international computer music conference, New Orleans, USA, Nov 2006. ICMA, San Francisco, pp 572–575Google Scholar
  83. 83.
    Dixon S, Goebl W, Widmer G (2002) The performance worm: real time visualisation of expression based on Langrer’s tempo-loudness animation. In: Proceedings of the international computer music conference, Goteborg, Sweden, Sept, pp 361–364Google Scholar
  84. 84.
    Sholkopf B, Smola A, Muller K (1998) Nonlinear component analysis as a kernel eigenvalue problem, Neural computation 10. MIT Press, Cambridge, MA, pp 1299–1319Google Scholar
  85. 85.
    Mitchell M (1998) Introduction to genetic algorithms. The MIT Press, CambridgeMATHGoogle Scholar
  86. 86.
    Kirke A (1997) Learning and co-operation in mobile multi-robot systems. PhD thesis, University of PlymouthGoogle Scholar
  87. 87.
    Chalmers D (2006) Strong and weak emergence. In: Clayton P, Davies P (eds) The re-emergence of emergence. Oxford University Press, OxfordGoogle Scholar
  88. 88.
    Ramirez R, Hazan A (2005) Modeling expressive performance in Jazz. In: Proceedings of 18th international Florida Artificial Intelligence Research Society conference (AI in Music and Art), Clearwater Beach, FL, USA, May 2005. AAAI Press, Menlo Park, pp 86–91Google Scholar
  89. 89.
    Zhang Q, Miranda ER (2006) Towards an interaction and evolution model of expressive music performance. In: Chen Y, Abraham A (eds) Proceedings of the 6th international conference on intelligent systems design and applications, Jinan, China, Oct 2006. IEEE Computer Society, Washington, DC, pp 1189–1194Google Scholar
  90. 90.
    Cambouropoulos E (2001) The local boundary detection model (LBDM) and its application in the study of expressive timing. In: Schloss R, Dannenberg R (eds) Proceedings of the 2001 international computer music conference, Havana, Cuba, Sept 2001. International Computer Music Association, San FranciscoGoogle Scholar
  91. 91.
    Krumhansl C (1991) Cognitive foundations of musical pitch. Oxford University Press, OxfordGoogle Scholar
  92. 92.
    Temperley D, Sleator D (1999) Modeling meter and harmony: a preference rule approach. Comput Music J 23:10–27CrossRefGoogle Scholar
  93. 93.
    Zhang Q, Miranda ER (2007) Evolving expressive music performance through interaction of artificial agent performers. In: Proceedings of ECAL 2007 workshop on music and artificial life (MusicAL 2007), Lisbon, Portugal, SeptGoogle Scholar
  94. 94.
    Miranda ER (2002) Emergent sound repertoires in virtual societies. Comput Music J 26(2):77–90CrossRefGoogle Scholar
  95. 95.
    Dannenberg RB (1993) A brief survey of music representation issues, techniques, and systems. Comput Music J 17:20–30CrossRefGoogle Scholar
  96. 96.
    Laurson M, Kuuskankare M (2003) From RTM-notation to ENP-score-notation. In: Proceedings of Journées d’Informatique Musicale 2003, Montbéliard, FranceGoogle Scholar
  97. 97.
    Bellini P, Nesi P (2001) WEDELMUSIC format: an XML music notation format for emerging applications. In: Proceedings of first international conference on web delivering of music, Florence, Nov 2001. IEEE Press, Los Alamitos, pp 79–86Google Scholar
  98. 98.
    Good M (2006) MusicXML in commercial applications. In: Hewlett WB, Selfridge-Field E (eds) Music analysis east and west. MIT Press, Cambridge, MA, pp 9–20Google Scholar
  99. 99.
    Atkinson JJS (2007) Bach: the Goldberg variations. Stereophile, Sept 2007Google Scholar
  100. 100.
    Toop R (1988) Four facets of the new complexity. Contact 32:4–50Google Scholar
  101. 101.
    Koelsch S, Siebel WA (2005) Towards a neural basis of music perception. Trends Cogn Sci 9:579–584CrossRefGoogle Scholar
  102. 102.
    Britton JC, Phan KL, Taylor SF, Welsch RC, Berridge KC, Liberzon I (2006) Neural correlates of social and nonsocial emotions: an fMRI study. Neuroimage 31:397–409CrossRefGoogle Scholar
  103. 103.
    Durrant S, Miranda ER, Hardoon D, Shawe-Taylor J, Brechmann A, Scheich H (2007) Neural correlates of tonality in music. In: Proceedings of music, brain, cognition workshop – NIPS Conference, Whistler, CanadaGoogle Scholar
  104. 104.
    Clarke EF (1993) Generativity, mimesis and the human body in music performance. Contemp Music Rev 9:207–219CrossRefGoogle Scholar
  105. 105.
    Parncutt R (1997) Modeling piano performance: physics and cognition of a virtual pianist. In: Cook PR (eds) Proceedings of 1997 international computer music conference, Thessalonikia, Greece, Sept 1997. ICMA, San Francisco, pp 15–18Google Scholar
  106. 106.
    Widmer G, Goebl W (2004) Computational models of expressive music performance: the state of the art. J New Music Res 33:203–216CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Faculty of ArtsInterdisciplinary Centre for Computer Music Research, Plymouth UniversityPlymouthUK

Personalised recommendations