102 (1) (2) (3) (4) 4 Shneiderman (i) Collect (ii) Relate (iii) Create (iv) Donate 4 4 (1) (4) [73] Collect Relate Create Donate ( C-R-C-D ) [14] [27]

101 Shneiderman Collect, Relate, Create, Donate 4 3 1 4 4 In this paper, we present a survey of automatic music generation systems from the viewpoint of creative process framework. According to Shneiderman s framework, a creative process consists of four phases: Collect, Relate, Create, and Donate. Regarding a system, a user, and a system designer as a total system, we investigate which of them achieves each of the four phases and how in existing music generation systems, especially music composition, arrangement, and performance rendering systems. Our investigation shows common and different parts in the four phases among the systems, which implied a direction to future music generation studies. 1 A Survey of Automatic Music Generation Systems basedoncreativeprocessframework. Masaki Matsubara,, Graduate School of Science and Technology, Keio University. Satoru Fukayama,, Graduate School of Information Science and Technology, the University of Tokyo. Kenta Okumura,, Graduate School of Engineering, Nagoya Institute of Technology. Keiko Teramura,, Graduate School of Informatics, Kyoto University. Hidefumi Ohmura, JST, ERATO,, Japan Science Technology Agency, ER- ATO, Okanoya Emotional Information Project. Mitsuyo Hashida,, SchoolofMusic, Soai University. Tetsuro Kitahara,, College of Humanities and Sciences, Nihon University., Vol.30, No.1 (2013),pp.101 118. [] 2012 3 31. 3 1 ( ) (1) BGM (2) 1

102 (1) (2) (3) (4) 4 Shneiderman (i) Collect (ii) Relate (iii) Create (iv) Donate 4 4 (1) (4) [73] Collect Relate Create Donate ( C-R-C-D ) [14] [27] [50] [96] 2 3 C-R-C-D 4 5 3 6 7 2 2. 1 16 [100] 1959 [45] ( [36]) ( [118]) () 18 19 [11] [53] () 1980 MIDI(Music Instrument Digital Interface) ( ) ( ) [79] PCM

Vol. 30 No. 1 Feb. 2013 103 1 Genex Phases Wallas Collect Relate Create Donate 1 Genex Phases(Shneiderman 2000) [50] 2. 2 1 Wallas 4 ( ) [91] Shneiderman Genex Phases [73] Genex Phases Wallas 1 4 Shneiderman Collect: Web Relate: Create: Donate: Web Shneiderman C-R-C-D 4 Donate Collect Shneiderman Wallas 1 (Collect) (Relate) (Create) ( ) (Donate) Donate 2. 3 3

104 3 C-R-C-D C-R-C-D C-R-C-D 3 C-R-C-D 3. 1 3 (1) (2) (3) (1) (2) (3) 3 (A) (B) (C) (1) (B) (2) (A) (1) (A) (B) (2) (A) (3) (C) (1) (2) (3) 3 Genex Phases 3 3. 2 (A) Stochastic Music Program [98] Project 1 [52] POD [89] [68] Stochastic Music Program [98] Project 1

Vol. 30 No. 1 Feb. 2013 105 [52] POD [89] [2] [66] [88] [9] [7] [68] (Collect) (Relate) Create 3. 3 Collect Relate Create [68] Stochastic Music Pro [98] Create Donate 3. 3 (B) 3. 3. 1 [45] Levitt style templates [55] Ebcioglu J. S. Bach 350 [31] [29] [72] [10] Collect Relate Collect Relate Donate Collect Relate Donate 3. 3. 2

106 D. Cope Cope [25] Collect Relate 3. 3. 3 Pachet Continuator N-gram [64] [77][110] Orpheus [36] 1 Pitman-Yor [111] Collect Relate Collect Relate (Collect) Collect Relate Create MIDI Donate 3. 4 (C) 1 ( ) Backtracking Specification Language [30] Constraint Logic Programming [43] [90] Constraint Satisfaction Techniques [63] Create Roads [68] GUI 1 Create Donate (IEC) [24] [99] (Create & Donate) [51] Create Donate IEC Create Create Donate

Vol. 30 No. 1 Feb. 2013 107 Cypher [69] 4 C-R-C-D 4. 1 3 3. 3 3 4. 2 ( if-then ) [103] [104] [61]GTTM(Generative Theory of Tonal Music) [54] [113] C-R-C-D (3.3.1 ) Collect Relate 4. 3 [102] () [114] Band-in-a-Box [65] ( ) (3.3.2 ) (Collect) Relate Relate 4. 4

108 [67][109] [44][58] [8][105] [118] [36] [120] C-R-C-D (3.3.3 ) ( [118]) Collect Relate ( [120]) Collect Relate 5 C-R-C-D 5. 1 [27][50][96] 3 3 C-R-C-D 5. 2 [13][22][23][33] [35][41][56][57][84] [87][101] Sundberg Director Musices [35][74] [13] [34][50] Todd [84] [87] Director Musices GTTM [54] Clynes [22]

Vol. 30 No. 1 Feb. 2013 109 [23] Mazzola [57] Mazzola Rubato Director Musices Collect Relate Collect Relate [34][35][74] [22][23] [57] [34][57] Create Donate Mazzola Rubato GUI [56] Director Musices pdm [33] Create Donate pdm 1 Mixtract [41] Relate Pop-E [101] [117] Relate 5. 3 [3] [6][46][75][76][80] [83][92] [94][97][106] Arcos SaxEx [3] [6] GTTM [59][60] (tender, aggressive, sad, joyful ) Kagurame [75][76][106] Ha-Hi-Hun [46] GTTM

110 ( ) Collect Relate [3] [6][46] ( Collect Relate Collect Relate Collect (Collect) 5. 4 [12][15] [21][32][47] [49][78][107][108] Music Interpretation System (MIS) [47][48][107][108] MIS Canazza CaRo [16] [21] 2 MIS Collect CaRo 2 Collect Relate MIS Create Donate pdm [33] MIS [50] Bresin ANN Piano [12] Director Musices

Vol. 30 No. 1 Feb. 2013 111 Collect Relate CaRo 2 Camurri Emotional Flute [15] Create Donate Widmer [80] [83][92] [94][97] Dorard KCCA Piano [28] 2 2 MIS Grindlay ESP Piano 2 Hierarchical HMM [37] 2 HHMM Flossman YQX [32][95] [59][60] Polyhymnia [49] Collect Relate Create MIS Usapi [78] Collect [32] Relate Collect ( ) 5. 5 C-R-C-D. ( ) Relate GTTM [54] [59][60] [117] [38] [40][119] Relate Sapp

112 [1][70][71] [62] Collect [26][42] Donate Rencon (Performance Rendering Contest) [112][121] 2 C-R-C-D 6 2 Create-Donate 6. 1 C-R-C-D 3 5 C-R-C-D C-R-C-D 2 (Collect Relate) (Collect) ( ) (Relate) Collect Create Donate 6. 2 Create-Donate [16] [21][33][51][56][69] Create Donate 3 ( 2) pdm [33] Cypher [69] Shneiderman [73] Create Donate Directability [115][116]

Vol. 30 No. 1 Feb. 2013 113 6. 3 2 Create Relate Collect Donate (Relate) (Create) (Donate) Collect Donate C-R-C-D Donate 6. 4 1 2 MIDI 7 Shneiderman C-R-C-D C-R-C-D C-R-C-D C-R-C-D C-R-C-D C-R-C-D

114 [ 1 ] AHRC Research Centre for the History and Analysis of Recorded Music: Mazurka Project, http: //www.mazurka.org.uk. [ 2 ] Ames,C.: TheMarkovProcessasaCompositional Model: A Survey and Tutorial, Leonardo, Vol. 22, No. 2(1989), pp. 175 187. [ 3 ] Arcos, J. and de Mantaras, R.: An interactive case-based reasoning approach for generating expressive music, Journal of Application for Intelligence, Vol. 14(2001), pp. 115 129. [ 4 ] Arcos, J. and demantaras, R.: The SaxEx system for expressive music synthesis: A progress report, in Proceedings of the Workshop on Current Research Directions in Computer Music, 2001, pp. 17 22. [ 5 ] Arcos, J., de Mantaras, R. and Serra, X.: SaxEx: A case-based reasoning system for generating expressive musical performances, in Proceedings of International Computer Music Conference (ICMC), 1997, pp. 329 336. [ 6 ] Arcos, J., de Mantaras, R. and Serra, X.: SaxEx: A case-based reasoning system for generating expressive musical performances, Journal of Mew Music Research, Vol. 27, No. 3(1998), pp. 194 210. [ 7 ] Bartlett, M.: A microcomputer-controlled synthesis system for live performance, Computer Music Journal, Vol. 3, No. 1(1979), pp. 25 37. [ 8 ] Bellgrad, M. O. and Tsuang, C. P.: Harmonizing Music the Boltzmann Way, Connection Science, Vol. 6(1994), pp. 281 297. [ 9 ] Beyls, P.: The musical universe of cellular automata, in Proceedings of International Computer Music Conference, 1989, pp. 34 41. [ 10 ] Biles, J. A.: GenJam: A Genetic Algorithm for Generating Jazz Solos, in Proceedings of International Computre Music Conference, 1994, pp. 131 137. [11] Boalch, D.: Makers of the Harpsichord and Clavichord, 1440 1840, Macmillan, New York, 1956. [ 12 ] Bresin, R.: Artificial neural networks based models for automatic performance of musical scores, Journal of New Music Research, Vol. 27, No. 3(1998), pp. 239 270. [ 13 ] Bresin, R. and Friberg, A.: Rule-based emotional coloring of music performance, in Proceedings of International Computer Music Conference (ICMC), 2000, pp. 364 367. [ 14 ] Buxton, W. A. S.: A Composer s Introduction to Computer Music, Interface, Vol. 6(1977), pp. 57 72. [ 15 ] Camurri, A., Dillon, R. and Saron, A.: An Experiment on Analysis and Synthesis of Musical Expressivity, in Proceedings of the 13th Colloquium on Musical Informatics, 2000. [ 16 ] Canazza, S., de Poli, G., Drioli, C., Roda, A., and Vidolin, A.: Expressive Morphing for Interavtive Performance of Musical Scores, in Proceedings of the 1st International Conference on WEB Delivering of Music, 2001, pp. 116 122. [ 17 ] Canazza, S., de Poli, G., Drioli, C., Roda, A. and Vidolin, A.: Modeling and Contol of Expressiveness in Music Performance, in Proceedings of IEEE 92, 2004, pp. 686 701. [ 18 ] Canazza, S., de Poli, G., Drioli, C., Roda, A. and Zamperni, F.: Real-time Morphing among Different Expressive Intentions on Audio Playback, in Proceedings of International Computer Music Conference (ICMC), 2000, pp. 356 359. [ 19 ] Canazza, S., de Poli, G., Roda, A. and Vidolin, A.: An Abstract Control Space for Communication of Sensory Expressive Intentions in Music Performance, Journal of New Music Research, Vol. 32(2003), pp. 281 294. [ 20 ] Canazza, S., de Poli, G., Roda, A., Vidolin, A. and Zanon, P.: Kinematics-Energy Space for Expressive Interaction on Music Performance, in Proceedings of the MOSART Workshop on Current Research Directions on Computer Music, 2001, pp. 35 40. [ 21 ] Canazza, S., Drioli, C., de Poli, G., Roda, A. and Vidolin, A.: Audio Morphing Different Expressive Intentions for Multimedia Systems, IEEE Multimedia, Vol. 7(2000), pp. 79 83. [ 22 ] Clynes, M.: Secrets of life in music, in Proceedings of International Computer Music Conference (ICMC), 1984, pp. 225 232. [ 23 ] Clynes, M.: Microstructural musical linguistics: Composer s pulses are linked most by the best musicians, COGNITION, International Journal of Cognitive Science, Vol. 55, No. 3(1995), pp. 269 310. [ 24 ] Conklin, D. and Witten, I. H.: Complexity- Based Induction, Machine Learning, Vol. 16, No. 3(1994), pp. 203 225. [25] Cope, D.: Machine models of music, MIT Press, 1992, pp. 403 425. [ 26 ] CrestMuse Project, JST/CREST, J.: Crest- Muse PEDB, http://www.crestmuse.jp/pedb. [ 27 ] de Mantaras, R. L. and Arcos, J. L.: AI and music: From composition to expressive performances, The AI Magazine, Vol. 23, No. 3(2002), pp. 43 57. [ 28 ] Dorard, L., Hardoon, D. R. and Shawe-Taylor, J.: Can Style be Learned? A Machine Learning Approach towards Performing as Famous Pianists, in Proceedings of the Music, Brain and Cognition Workshop in The Neural Information Processing Systems, 2007. [ 29 ] Ebcioglu, K.: Computer Counterpoint, in Proceedings of International Computer Music Conference, 1980, pp. 534 543. [ 30 ] Ebcioglu, K.: Computer Counterpoint, in Proceedings of the 1986 Association for the Advancement of Artificial Intelligence (AAAI) Conference, 1986.

Vol. 30 No. 1 Feb. 2013 115 [ 31 ] Ebcioglu, K.: An Expert System for Harmonizing Four-Part Chorales, Computer Music Journal, Vol. 12, No. 3(1988), pp. 43 51. [ 32 ] Flossman, S., Grachten, M. and Widmer, G.: Expressive Performance Rendering: Introducing Performance Context, in Proceedings of Sound and Music Computing (SMC) Conference, 2009, pp. 155 160. [ 33 ] Friberg, A.: pdm: An Expressive Sequencer with Real-Time Control of the KTH Music- Performance Rules, Computaer Music Journal, Vol. 30, No. 1(2006), pp. 37 48. [ 34 ] Friberg, A., Bresin, R. and Sundberg, J.: Overview of the KTH rule system for musical performance, Advances in Cognitive Psychology, Vol. 2, No. 2 3(2006), pp. 145 161. [35] Frydén, L. and Sundberg, J.: Performance rules for melodies. origin, functions, purposes, in Proceedings of International Computer Music Conference (ICMC), 1984, pp. 221 225. [ 36 ] Fukayama, S., Nakatsuma, K., Sako, S., Nishimoto, T. and Sagayama, S.: Automatic song composition from the lyrics exploiting prosody of Japanese language, in Proceedings Sound and Music Computing (SMC) Conference, 2010. [ 37 ] Grindlay, G. and Helmbold, D.: Modeling, Analyzing and Synthesizing Expressive Piano Performance with Graphical Models, Machine Learning Journal, Vol. 65, No. 2 3(2006), pp. 361 387. [38] Hamanaka,M.,Hirata,K.andTojo,S.:Automatic Generation of Grouping Structure based on the GTTM, in Proceedings of International Computer Music conference (ICMC), 2004, pp. 141 144. [ 39 ] Hamanaka, M., Hirata, K. and Tojo, S.: Implementing A Generating Theory of Tonal Music, Jornal of New Music Reserch, Vol. 35, No. 4(2006), pp. 249 277. [ 40 ] Hamanaka, M. and Tojo, S.: Interactive GTTM Analyzer, in Proceedings of the 10th International Society for Music Information Retrieval (ISMIR) Conference, 2009, pp. 291 296. [ 41 ] Hashida, M. and Katayose, H.: Mixtract: and environment for designing musical phrase expression, in Proceedings of Sound and Music Computing (SMC) Conference, 2010. [ 42 ] Hashida, M., Matsui, T. and Katayose, H.: A New Music Database Describing Deviation Information of Performance Expressions, in Proceedings of the 9th International Society for Music Information Retrieval (ISMIR) Conference, 2008, pp. 489 494. [ 43 ] Henz, M., Lauer, S. and Zimmermann, D.: COMPOzE Intention-based Music Composition through Constraint Programming, in Proceedings of the 8th International Conference on Tools with Artificial Intelligence (ICTAI), Washington, DC, USA, IEEE Computer Society, 1996, pp. 118 121. [ 44 ] Hild, H., Feulner, J. and Menzel, W.: HAR- MONET: A neural net for harmonizing chorales in the style of J. S. Bach, Advances in Neural Information Processing (NIPS), Morgan Kaufmann, 1991, pp. 267 274. [ 45 ] Hiller, L. and Isaacson, L.: Musical composition with a high-speed digital computer, Journal of the Audio Engineering Society, Vol. 6, No. 3(1958), pp. 154 160. [ 46 ] Hirata, K. and Hiraga, R.: Ha-Hi-Hun: Performance rendering system of high controllability, in Proceedings of Rencon Workshop in International Conference on Auditory Display (ICAD), 2002, pp. 40 46. [ 47 ] Katayose, H. and Inokuchi, S.: Kansei music system, Computer Music Journal, Vol. 13, No. 4(1990), pp. 72 77. [ 48 ] Katayose, H., Uwabu, U. and Ishikawa, O.: A music interpretation system - schema acquisition and performance rule extraction, in Proceedings of ICAD-Rencon: Performance Rendering Systems: Today and Tomorrow, 2002, pp. 7 12. [ 49 ] Kim, T., Fukayama, S., Nishimoto, T. and Sagayama, S.: Performance Rendering for Polyphonic Piano Music with a Combination of Probabilistic Models for Melody and Harmony, in Proceedings of Sound and Music Computing (SMC) Conference, 2010. [ 50 ] Kirke, A. and Miranda, E. R.: A survey of computer systems for expressive music performance, ACM Computing Surveys (CSUR), Vol. 42, No. 1(2009), pp. 1 41. [ 51 ] Kitahara, T., Fukayama, S., Sagayama, S., Katayose, H. and Nagata, N.: An interactive music composition system based on autonomous maintenance of musical consistency, in Proceedings of Sound and Music Computing (SMC) Conference, 2011. [ 52 ] Laske, O.: Composition Theory in Koenig s Project One and Project Two, Computer Music Journal, Vol. 5, No. 4(1981), pp. 54 65. [ 53 ] Leichtentritt, H.: Mechanical music in olden times, Musical Quarterly, Vol. 20, No. 1(1934), pp. 15 26. [ 54 ] Lerdahl, F. and Jackendoff, R.: A Generative Theory of Tonal Music, MIT Press, Cambridge, MA, 1983. [ 55 ] Levitt, D.: Machine Tongues X: Constraint Languages, Computer Music Journal, Vol.8, No.1 (1984), pp. 9 21. [ 56 ] Mazzola, G.: The Topos of Music Geometric Logic of Concepts, Theory and Performance, Birkhäuser, Basel, Switzerland/Boston, MA, 2002. [ 57 ] Mazzola, G. and Zahorka, O.: Tempo curves revisited: Hierarchies of performance fields, Computer Music Journal, Vol. 18, No. 1(1994), pp. 40 52. [ 58 ] Mozer, M. C.: Neural Network Music Composition by Prediction: Exploring the Benefits of Psychoacoustic Constraints and Multi-scale Processing, Connection Science, Vol. 6(1994), pp. 247 280.

116 [ 59 ] Narmour, E.: The analysis and cognition of basic melodic structures: the implication-realization model, University of Chicago Press, 1990. [ 60 ] Narmour, E.: The analysis and cognition of melodic complexity: the implication-realization model, University of Chicago Press, 1992. [ 61 ] Niitsuma, M., Matsubara, M., Oono, M. and Saito, H.: Development of a method for automatic basso continuo playing, Journal of Information Processing and Management, Vol. 47, No. 3(2011), pp. 440 451. [ 62 ] Okumura, K., Sako, S. and Kitamura, T.: Stochastic Modeling of a Musical Performance with Expressive Representations from the Musical Score, in Proceedings of the 12th International Society for Music Information Retrieval (ISMIR) conference, 2011, pp. 531 536. [ 63 ] Pachet, F. and Roy, P.: Integrating constraint satisfaction techniques with complex object structures, in Proceedings of 15th Annual Conference of the British Computer Society Specialist Group on Expert Systems, ES 95, Cambridge, U.K., 1995, pp. 11 22. Best technical paper award. [ 64 ] Pachet, F.: The Continuator: Musical Interaction With Style, Journal of New Music Research, Vol. 32, No. 3(2003), pp. 333 341. [ 65 ] PG Music: Band-in-a-Box, http://www. pgmusic.com. [ 66 ] Pressing, J.: Nonlinear maps as generators of musical design, Computer Music Journal, Vol.12, No. 2(1988), pp. 35 46. [ 67 ] Raphael, C. and Stoddard, J.: Harmonic analysis with probabilistic graphical models, in Proceedings of the 4th International Society for Music Informatics Retrieval (ISMIR) Conference, 2003. [68] Roads, C.: The Computer Music Tutorial, MIT Press, Cambridge, MA, 1996. [69] Rowe,R.: Interactive Music Systems Machine Listening and Composing, The MIT Press, 1993. [ 70 ] Sapp, C. S.: Comparative analysis of multiple musical performances, in Proceedings of the 8th International Society for Music Information Retrieval (ISMIR) conference, 2007, pp. 497 500. [ 71 ] Sapp, C. S.: Harmonic Visualizations of Tonal Music, in Proceedings of International Computer Music Conference (ICMC), Havana, Cuba, 2001, pp. 423 430. [ 72 ] Schottstaedt, W.: Current Directions in Computer Music Research, The MIT Press, 1989, pp. 215 224. [ 73 ] Shneiderman, B.: Creating Creativity: User Interfaces for Supporting Innovation, ACM Transactions on Computer-Human Interaction (ToCHI), Vol. 7, No. 1(2000), pp. 114 138. [ 74 ] Sundberg, J., Askenfelt, A. and Frydén, L.: Musical Performance. A synthesis-by-rule approach, Computer Music Journal, Vol. 7(1983), pp. 37 43. [ 75 ] Suzuki, T.: Kagurame Phase-II, in Proceedings of Workshop on methoods for automatic music performance and their applications in a public rendering contest, International Joint Conference of Artificial Intelligent (IJCAI), Gottlob, G. and Walsh, T.(eds.), San Francisco, CA, Eds. Morgan Kauffman, 2003, pp. 78 81. [ 76 ] Suzuki, T., Tokunaga, T. and Tanaka, H.: A case based approach to the generation of musical expression, in Proceedings of the 16th international joint conference on Artificial Intelligence, 1999, pp. 642 648. [ 77 ] Tanaka, T., Nishimoto, T., Ono, N. and Sagayama, S.: Automatic music composition based on counterpoint and imitation using stochastic models, in Proceedings of Sound and Music Computing (SMC) Conference, 2010. [ 78 ] Teramura, K., Okuma, H., Tahiguchi, Y., Makimoto, S. and Maeda, S.: Gaussian Process Regression for Rendering Musc Performance, in Proceedings of International Conference on Music Perception and Cognition (ICMPC), 2008, pp. 167 172. [ 79 ] Tobenfeld, E.: A general-purpose sequncer for MIDI synthesizers, Computer Music Journal, Vol.8, No. 4(1984), pp. 43 54. [ 80 ] Tobudic, A. and Widmer, G.: Learning to Play Mozart: Recent Improvements, in Proceedings of Workshop on methoods for automatic music performance and their applications in a public rendering contest, International Joint Conference of Artificial Intelligent (IJCAI), 2003, pp. 37 45. [ 81 ] Tobudic, A. and Widmer, G.: Relational IBL in Music with a New Structural Similarity Measure, in Proceedings of the 13th International Congerence on Inductive Logic Programming, 2003, pp. 365 382. [ 82 ] Tobudic, A. and Widmer, G.: Technical Notes for Musical Contest Category, in Proceedings of Workshop on methoods for automatic music performance and their applications in a public rendering contest, International Joint Conference of Artificial Intelligent (IJCAI), 2003, pp. 85 87. [ 83 ] Tobudic, A. and Widmer, G.: Learning to Play Like the Great Pianists, in Proceedings of the International Joint Conference on Artificial Intelligence, 2005, pp. 871 876. [ 84 ] Todd, P. M.: A model of expressive timing in tonal music, Music Perception, Vol. 3(1985), pp. 33 58. [ 85 ] Todd, P. M.: A computational model of Rubato, Contemporary Music Rev., Vol. 3(1989), pp. 69 88. [ 86 ] Todd, P. M.: The dynamics of dynamics: A model of musical expression, Journal of Acoustical Society of America, Vol. 91(1992), pp. 3940 3950. [ 87 ] Todd, P. M.: The kinematics of musical expression, Journal of Acoustical Society of America, Vol. 97(1995), pp. 1940 1949. [ 88 ] Todd, P. M. and Werner, G. M.: Musical Net-

Vol. 30 No. 1 Feb. 2013 117 works, MIT Press, 1999, pp. 313 339. [ 89 ] Truax, B.: The POD system of interactive composition programs, Computer Music Journal, Vol. 1, No. 3(1977), pp. 30 39. [ 90 ] Tsang, C. and Aitken, M.: Harmonizing Music as a Discipline of Constraint Logic Programming, in Proceedings of the 15th International Computer Music Conference, Montréal, Canada, 1991, pp. 61 64. [ 91 ] Wallas, G.: Art of Thought, Harcort Brace, New York, 1925. [ 92 ] Widmer, G.: Large-Scale Induction of Expressive Performance Rules: First quantitative Results, in Proceedings of International Computer Music Conference (ICMC), 2000, pp. 344 347. [ 93 ] Widmer, G.: Machine Discoveries: A Few Simple, Robust Local Expression Principles, Journal of New Music Research, Vol. 31(2002), pp. 37 50. [ 94 ] Widmer, G.: Discovering Simple Rules in Complex Data: A Meta-Learning Algorithm and Somce Surprising Musical Discoveries, Artificial Intelligence, Vol. 146(2003), pp. 129 148. [ 95 ] Widmer, G., Flossman, S. and Grachten, M.: YQX plays chopin, The AI Magazine, Vol.30, No. 3(2009), pp. 35 48. [ 96 ] Widmer, G. and Goebl, W.: Computational Models of Expressive Music Performance: The State of the Art, Journal of New Music Research, Vol.33, No. 3(2004), pp. 203 216. [ 97 ] Widmer, G. and Tobudic, A.: Playing Mozart by Analogy: Learning Multi-Level Timing and Dynamics Strategies, Journal of New Music Research, Vol. 32(2003), pp. 203 216. [98] Xenakis, I.: Formalized Music, Boomington, Indiana University Press, 1971. [99],,, : ( VI), 2008-MUS-76,, 2008. [100], :,, 2004. [101],,, :,, Vol. 48, No. 1(2007), pp. 248 257. [102], :, 96-MUS-16,, 1996. [103],, :, MA2007-32,, 2007. [104],,,,, : : AMOR,, Vol. 46, No. 5(2005), pp. 1176 1187. [105],, :,, Vol.108, No. 480(2009), pp. 411 416. [106],,,,, :, 2007-MUS-72,, 2007. [107],, :,, Vol. 38, No. 7(1997), pp. 1473 1481. [108],, :,, Vol. 43, No. 2(2002), pp. 268 276. [109],,, :, 99-MUS-34,, 2000. [110],, :, 2004- MUS-56 10,, 2004. [111], : Pitman-Yor, 2011-MUS-91 3,, 2011. [112],, :,, Vol. 43, No. 2(2002), pp. 136 141. [113], : : GTTM, 2002-MUS-46,, 2002. [114], :,, Vol. 42, No. 3(2001), pp. 633 641. [115] : ( ),, Vol. 48, No. 12(2007), pp. 1359 1364. [116], : Directability, 2007-MUS-81,, 2007. [117] :,, 1998. [118],,, :,, Vol. 50, No. 3(2009), pp. 1067 1078. [119],,,, :, 2010-MUS-87,, 2010. [120],,, :,, No. 3-6-15(2012), pp. 1057 1058. [121] (SIGMUS) : Musical Performance Rendering Contest for Computer Systems, http://renconmusic.org.

118 2006 2012 2008 2010 2011 (DC-2) 2009 2007 2009 2007 ( ) 2009 2012 2002 2009 ( ) JST ERATO 2010 2001 2006 ( ) ( ) JST CREST 2002 2007 ( ) (DC2) JST CREST 2