Dorien Herremans
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Abstract
Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.
- K. Agres, J. E. DeLong, and M. Spivey. 2009. The sparsity of simple recurrent networks in musical structure learning. In Proceedings of the 31st Annual Conference of the Cognitive Science Society. 3099--3104.Google Scholar
- K. Agres, J. Forth, and G. A. Wiggins. 2017. Evaluation of musical creativity and musical metacreation systems. Comput. Entertain. 14, 3, Article 3 (Jan. 2017), 33 pages.Google Scholar
- K. Agres, D. Herremans, L. Bigo, and D. Conklin. 2016. Harmonic structure predicts the enjoyment of uplifting trance music. Frontiers Psychol. 7 (2016), 1999.Google Scholar
- G. Aguilera, J. Luis Galán, R. Madrid, A. M. Martínez, Y. Padilla, and P. Rodríguez. 2010. Automated generation of contrapuntal musical compositions using probabilistic logic in Derive. Math. Comput. Simul. 80, 6 (2010), 1200--1211. Google ScholarDigital Library
- M. Allan and C. K. I. Williams. 2005. Harmonising chorales by probabilistic inference. Adv. Neural Info. Process. Syst. 17 (2005), 25--32.Google Scholar
- C. Allauzen, M. Crochemore, and M. Raffinot. 1999. Factor oracle: A new structure for pattern matching. In Proceedings of the International Conference on Current Trends in Theory and Practice of Computer Science. Springer, 295--310. Google ScholarCross Ref
- A. Alpern. 1995. Techniques for algorithmic composition of music. Retrieved from http://hamp.hampshire.edu/∼/algocomp/algocomp95.Google Scholar
- T. Anders. 2007. Composing Music by Composing Rules: Design and Usage of a Generic Music Constraint System. Ph.D. Dissertation. Queen’s University Belfast.Google Scholar
- T. Anders and E. R. Miranda. 2009. A computational model that generalises Schoenbergs guidelines for favourable chord progressions. In Proceedings of the Sound and Music Computing Conference. 48--52.Google Scholar
- C. Ariza. 2002. Prokaryotic groove: Rhythmic cycles as real-value encoded genetic algorithms. In Proceedings of the International Computer Music Conference (ICMC’02). 561--568.Google Scholar
- G. Assayag, G. Bloch, and M. Chemillier. 2006. OMax-ofon. Proceedings of the Conference on Sound and Music Computing (SMC’06).Google Scholar
- G. Assayag, G. Bloch, A. Cont, and S. Dubnov. 2010. Interaction with machine improvisation. In The Structure of Style. Springer, 219--245. Google ScholarCross Ref
- G. Assayag, S. Dubnov, and O. Delerue. 1999a. Guessing the composer’s mind. In Proceedings of the International Computer Music Conference (ICMC’99). Bejing, China, 1--1.Google Scholar
- G. Assayag, C. Rueda, M. Laurson, C. Agon, and O. Delerue. 1999b. Computer-assisted composition at IRCAM: From patchwork to openmusic. Comput. Music J. 23, 3 (1999), 59--72. Google ScholarDigital Library
- M. Balliauw, D. Herremans, D. Palhazi Cuervo, and K. Sörensen. 2017. A variable neighbourhood search algorithm to generate piano fingerings for polyphonic sheet music. Int. Trans. Operat. Res: Special Issue on Variable Neighbourhood Search 24, 3 (2017), 509--535.Google ScholarCross Ref
- L. E. Baum and T. Petrie. 1966. Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Stat. 37, 6 (1966), 1554--1563. Google ScholarCross Ref
- B. Bemman and D. Meredith. 2016. Generating milton babbitts all-partition arrays. J. New Music Res. 45, 2 (2016), 184--204. Google ScholarCross Ref
- Y. Bengio, P. Simard, and P. Frasconi. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5, 2 (1994), 157--166. Google ScholarDigital Library
- L. Bigo and D. Conklin. 2015. A viewpoint approach to symbolic music transformation. In Proceedings of the International Symposium on Computer Music Multidisciplinary Research. Springer, 213--227.Google Scholar
- J. A. Biles. 1998. Interactive GenJam: Integrating real-time performance with a genetic algorithm. In Proceedings of the 1998 International Computer Music Conference. 232--235.Google Scholar
- J. A. Biles. 2001. Autonomous GenJam: Eliminating the fitness bottleneck by eliminating fitness. In Proceedings of the GECCO-2001 Workshop on Non-routine Design with Evolutionary Systems. Morgan Kaufmann, San Francisco, CA.Google Scholar
- G. Bloch, S. Dubnov, and G. Assayag. 2008. Introducing video features and spectral descriptors in the OMax improvisation system. In Proceedings of the International Computer Music Conference, Vol. 8.Google Scholar
- N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent. 2012. Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In Proceedings of the 29th International Conference on Machine Learning (ICML’12), John Langford and Joelle Pineau (Eds.). ACM, New York, NY, 1159--1166.Google Scholar
- R. Bresin and A. Friberg. 2000. Emotional coloring of computer-controlled music performances. Comput. Music J. 24, 4 (2000), 44--63. Google ScholarDigital Library
- F. P. Brooks, A. L. Hopkins, P. G. Neumann, and W. V. Wright. 1957. An experiment in musical composition. IRE Trans. Elect. Comput. 3 (1957), 175--182. Google ScholarCross Ref
- D. Brown. 2012. Mezzo: An adaptive, real-time composition program for game soundtracks. In Proceedings of the AIIDE Workshop on Musical Metacreativity.Google Scholar
- T. M. Browne and C. Fox. 2009. Global expectation-violation as fitness function in evolutionary composition. In Applications of Evolutionary Computing. Springer, 538--546. Google ScholarDigital Library
- G. Carpentier, G. Assayag, and E. Saint-James. 2010. Solving the musical orchestration problem using multiobjective constrained optimization with a genetic local search approach. J. Heurist. 16, 5 (2010), 1--34. Google ScholarDigital Library
- P. Casella and A. Paiva. 2001. Magenta: An architecture for real time automatic composition of background music. In Intelligent Virtual Agents. Springer, 224--232. Google ScholarCross Ref
- M. Chemillier. 2001. Improvising jazz chord sequences by means of formal grammars. In Journées d’informatique Musicale. 121--126.Google Scholar
- E. Chew. 2014. Mathematical and Computational Modeling of Tonality: Theory and Applications. Vol. 204. Springer, New York. Google ScholarCross Ref
- C.-H. Chuan and E. Chew. 2011. Generating and evaluating musical harmonizations that emulate style. Comput. Music J. 35, 4 (2011), 64--82. Google ScholarDigital Library
- K. Collins. 2008. Game Sound: An Introduction to the History, Theory, and Practice of Video Game Music and Sound Design. MIT Press.Google ScholarCross Ref
- K. Collins. 2009. An introduction to procedural music in video games. Contemp. Music Rev. 28, 1 (2009), 5--15. Google ScholarCross Ref
- N. Collins. 2012. Automatic composition of electroacoustic art music utilizing machine listening. Comput. Music J. 36, 3 (2012), 8--23. Google ScholarDigital Library
- D. Conklin. 2003. Music generation from statistical models. In Proceedings of the AISB Symposium on Artificial Intelligence and Creativity in the Arts and Sciences. Aberystwyth, Wales, 30--35.Google Scholar
- D. Conklin and I. Witten. 1995. Multiple viewpoint systems for music prediction. J. New Music Res. 24, 1 (1995), 51--73. Google ScholarCross Ref
- D. Cope. 1996. Experiments in Musical Intelligence. Vol. 12. AR Editions, Madison, WI.Google Scholar
- D. Cope. 2000. New Directions in Music. Waveland Press.Google Scholar
- D. Cope. 2004. A musical learning algorithm. Comput. Music J. 28, 3 (2004), 12--27. Google ScholarDigital Library
- N. Cunha, A. Subramanian, and D. Herremans. 2016. Uma abordagem baseada em programação linear inteira para a geração de solos de guitarra. XLVIII Simpósio Brasileiro de Pesquisa Operacional (SBPO) (09/2016 2016).Google Scholar
- R. B. Dannenberg and E. Brandt. 1996. A flexible real-time software synthesis system. In Proceedings of the International Computer Music Conference, International Computer Music Association. 270--273.Google Scholar
- S. Davismoon and J. Eccles. 2010. Combining musical constraints with Markov transition probabilities to improve the generation of creative musical structures. In Applications of Evolutionary Computation. Springer, 361--370. Google ScholarDigital Library
- R. De Prisco, A. Eletto, A. Torre, and R. Zaccagnino. 2010. A neural network for bass functional harmonization. Appl. Evolution. Comput. 6025 (2010), 351--360.Google ScholarDigital Library
- R. De Prisco, G. Zaccagnino, and R. Zaccagnino. 2012. A differential evolution algorithm assisted by ANFIS for music fingering. In Swarm and Evolutionary Computation. Springer, 48--56. Google ScholarDigital Library
- E. Deruty. 2016. Goal-oriented mixing. In Proceedings of the 2nd AES Workshop on Intelligent Music Production, Vol. 13. London.Google Scholar
- M. Dostál. 2013. Evolutionary music composition. In Handbook of Optimization. Springer, 935--964.Google Scholar
- S. Dubnov. 2006. Spectral anticipations. Comput. Music J. 30, 2 (2006), 63--83. Google ScholarDigital Library
- S. Dubnov and G. Assayag. 2012. Music design with audio oracle using information rate. In Proceedings of the Conference on Musical Metacreation: Papers from the 2012 AIIDE Workshop.Google Scholar
- M. O. Duff. 1989. Backpropagation and bachs 5th cello suite (Sarabande). In Proceedings of the International Joint Conference on Neural Networks. 575. Google ScholarCross Ref
- K. Ebcioğlu. 1988. An expert system for harmonizing four-part chorales. Comput. Music J. 12, 3 (1988), 43--51.Google ScholarCross Ref
- D. Eck and J. Schmidhuber. 2002. A first look at music composition using lstm recurrent neural networks. Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale (2002).Google Scholar
- A. Eigenfeldt and P. Pasquier. 2009. A realtime generative music system using autonomous melody, harmony, and rhythm agents. In Proceedings of the 12th Generative Art Conference (GA’09).Google Scholar
- M. M. Farbood. 2012. A parametric, temporal model of musical tension. Music Percept. 29, 4 (2012), 387--428. Google ScholarCross Ref
- M. Farbood, H. Kaufman, and K. Jennings. 2007. Composing with hyperscore: An intuitive interface for visualizing musical structure. In Proceedings of the International Computer Music Conference (ICMC’07), Vol. 59.Google Scholar
- M. Farbood and B. Schoner. 2001. Analysis and synthesis of palestrina-style counterpoint using markov chains. In Proceedings of the International Computer Music Conference. 471--474.Google Scholar
- J. D. Fernández and F. Vico. 2013. AI methods in algorithmic composition: A comprehensive survey. J. Artific. Intell. Res. (2013), 513--582.Google Scholar
- A. R. J. François, E. Chew, and D. Thurmond. 2007. Visual feedback in performer-machine interaction for musical improvisation. In Proceedings of the 7 International Conference on New Interfaces for Musical Expression. ACM, 277--280.Google Scholar
- A. R. J. François, I. Schankler, and E. Chew. 2013. Mimi4x: An interactive audio-visual installation for high-level structural improvisation. Int. J. Arts Technol. 6, 2 (2013), 138--151.Google ScholarCross Ref
- J. A. Franklin. 2001. Multi-phase learning for jazz improvisation and interaction. In Proceedings of the 8th Biennial Symposium for Arts and Technology.Google Scholar
- J. A. Franklin. 2006. Recurrent neural networks for music computation. INFORMS J. Comput. 18, 3 (2006), 321--338. Google ScholarDigital Library
- J. J. Fux and A. Mann. 1971. The Study of Counterpoint from Johann Joseph Fux’s Gradus Ad Parnassum—1725. Norton, New York.Google Scholar
- M. Geis and M. Middendorf. 2007. An ant colony optimizer for melody creation with baroque harmony. In Proceedings of the IEEE Congress on Evolutionary Computation. 461--468. Google ScholarCross Ref
- S. Gherman. 2008. Harmony and its functionality: A gloss on the substantial similarity test in music copyrights. Fordham Intell. Prop. Media Ent. LJ 19 (2008), 483.Google Scholar
- J. Gillick, K. Tang, and R. M. Keller. 2010. Machine learning of jazz grammars. Comput. Music J. 34, 3 (2010), 56--66. Google ScholarDigital Library
- M. Grachten, C. E. Cancino Chacón, and G. Widmer. 2014. Analysis and prediction of expressive dynamics using Bayesian linear models. In Proceedings of the 1st International Workshop on Computer and Robotic Systems for Automatic Music Performance. 545--552.Google Scholar
- A. Hawryshkewich, P. Pasquier, and A. Eigenfeldt. 2011. Beatback: A real-time interactive percussion system for rhythmic practise and exploration. In Proceedings of the NIME Conference. 100--105.Google Scholar
- S. A. Hedges. 1978. Dice music in the eighteenth century. Music Lett. 59, 2 (1978), 180--187. Google ScholarCross Ref
- D. Herremans and E. Chew. 2016a. MorpheuS: Automatic music generation with recurrent pattern constraints and tension profiles. Proceedings of the IEEE TENCON (November 2016). Singapore. Google ScholarCross Ref
- D. Herremans and E. Chew. 2016b. Tension ribbons: Quantifying and visualising tonal tension. In Proceedings of the 2nd International Conference on Technologies for Music Notation and Representation (TENOR’16). Cambridge, UK.Google Scholar
- D. Herremans and E. Chew. 2017. MorpheuS: Generating structured music with constrained patterns and tension. IEEE Transactions on Affective Computing. PP (in press). (2017). DOI:10.1109/TAFFC.2017.2737984 Google ScholarCross Ref
- D. Herremans and C.-H. Chuan. 2017. Modeling musical context with word2vec. Proceedings of the 1st International Workshop on Deep Learning and Music 1 (05/2017 2017), Anchorage, US, 11--18.Google Scholar
- D. Herremans and K. Sörensen. 2012. Composing first species counterpoint with a variable neighbourhood search algorithm. J. Math. Arts 6, 4 (2012), 169--189.Google ScholarCross Ref
- D Herremans and K Sörensen. 2013. Composing fifth species counterpoint music with a variable neighborhood search algorithm. Expert Syst. Appl. 40, 16 (2013), 6427--6437. Google ScholarDigital Library
- D. Herremans and K. Sorensen. 2013. FuX, an android app that generates counterpoint. In Proceedings of the IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC’13). Singapore, 48--55. Google ScholarDigital Library
- D. Herremans, K. Sörensen, and D. Martens. 2015a. Classification and generation of composer-specific music using global feature models and variable neighborhood search. Computer Music Journal 39 (2015), 71--91. Google ScholarDigital Library
- D. Herremans, S. Weisser, K. Sörensen, and D. Conklin. 2015b. Generating structured music for bagana using quality metrics based on Markov models. Expert Syst. Appl. 42, 21 (2015), 7424--7435.Google ScholarDigital Library
- H. Hild, J. Feulner, and W. Menzel. 1992. HARMONET: A neural net for harmonizing chorales in the style of JS Bach. In Adv. Neural Info. Process. Syst. 267--274.Google Scholar
- L. Hiller. 1989. Lejaren Hiller: Computer Music Retrospective (1957--1985). Wergo Schallplatten. 79 pages.Google Scholar
- A. Hiller Jr, L. and L. M. Isaacson. 1957. Musical composition with a high speed digital computer. In Audio Engineering Society Convention 9. Audio Engineering Society. Google ScholarDigital Library
- S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735--1780.Google ScholarDigital Library
- J. H. Holland. 1992. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press.Google Scholar
- A. Horner and D. E. Goldberg. 1991. Genetic algorithms and computer-assisted music composition. Urbana 51, 61801 (1991), 437--441.Google Scholar
- C. Z. A. Huang and E. Chew. 2005. Palestrina pal: A grammar checker for music compositions in the style of Palestrina. In Proceedings of the 5th Conference on Understanding and Creating Music.Google Scholar
- T. A. Hummel. 2005. Simulation of human voice timbre by orchestration of acoustic music instruments. In Proceedings of International Computer Music Conference (ICMC’05). 185.Google Scholar
- D. B. Huron. 2006. Sweet Anticipation: Music and the Psychology of Expectation. MIT press.Google Scholar
- S. Johnson. 2006. The long zoom. New York Times Magazine (2006), 50--55.Google Scholar
- R. M. Keller and D. R. Morrison. 2007. A grammatical approach to automatic improvisation. In Proceedings of the 4th Sound and Music Conference, Lefkada, Greece, July. 330--336. Google ScholarCross Ref
- S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. 1983. Optimization by simulated annealing. Science 220, 4598 (1983), 671--680.Google Scholar
- J. P. Kirnberger. 1757. Der allezeit fertige menuetten-und polonoisenkomponist. Berlin: Germany Winter (1757). Google ScholarCross Ref
- C. L. Krumhansl. 2001. Cognitive Foundations of Musical Pitch. Oxford University Press. Google ScholarCross Ref
- S. Lattner, M. Grachten, K. Agres, and C. E. C. Chacón. 2015. Probabilistic segmentation of musical sequences using restricted Boltzmann machines. In Proceedings of the International Conference on Mathematics and Computation in Music. Springer, 323--334.Google ScholarCross Ref
- H.-R. Lee and J. S. R. Jang. 2004. i-Ring: A system for humming transcription and chord generation. In Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME’04). Vol. 2. IEEE, 1031--1034. Google ScholarCross Ref
- F. Lerdahl and R. Jackendoff. 1983. An overview of hierarchical structure in music. Music Percept.: Interdisc. J. 1, 2 (1983), 229--252. Google ScholarCross Ref
- G. E. Lewis. 2000. Too many notes: Computers, complexity and culture in voyager. Leonardo Music J. 10 (2000), 33--39.Google ScholarCross Ref
- J. P. Lewis. 1991. Creation by refinement and the problem of algorithmic music composition. Music and Connectionism (1991), 212. Google ScholarCross Ref
- T. R. Lezon, J. R. Banavar, M. Cieplak, A. Maritan, and N. V. Fedoroff. 2006. Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns. Proc. Natl. Acad. Sci. U.S.A. 103, 50 (2006), 19033--19038.Google ScholarCross Ref
- Y. J. Liebesman. 2007. Using innovative technologies to analyze for similarity between musical works in copyright infringement disputes. AIPLA Quart. J. 35 (2007), 331. Google ScholarDigital Library
- C.-C. Lin and D. S.-M. Liu. 2006. An intelligent virtual piano tutor. In Proceedings of the 2006 ACM International Conference on Virtual Reality Continuum and its Applications. ACM, 353--356.Google ScholarDigital Library
- A. Lovelace. 1843. Notes on L. Menabreas sketch of the analytical engine invented by Charles Babbage, Esq.Taylors Sci. Mem. 3 (1843).Google Scholar
- M. Marchini and H. Purwins. 2010. Unsupervised generation of percussion sound sequences from a sound example. In Proceedings of the Sound and Music Computing Conference, Vol. 220.Google Scholar
- A. Marsden. 2004. Novagen: A combination of Eyesweb and an elaboration-network representation for the generation of melodies under gestural control. In Proceedings of the ConGAS Symposium on Gesture Interfaces for Multimedia Systems.Google Scholar
- J. McCormack. 1996. Grammar based music composition. Complex Syst. 96 (1996), 321--336. Google ScholarCross Ref
- W. S. McCulloch and W. Pitts. 1943. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 4 (1943), 115--133. Google ScholarCross Ref
- R. A. McIntyre. 1994. Bach in a box: The evolution of four part baroque harmony using the genetic algorithm. In Proceedings of the 1994 IEEE World Congress on Computational Intelligence: Evolutionary Computation. IEEE, 852--857.Google ScholarCross Ref
- M. McVicar, S. Fukayama, and M. Goto. 2014. AutoLeadGuitar: Automatic generation of guitar solo phrases in the tablature space. In Proceedings of the 2014 12th International Conference on Signal Processing (ICSP’14). IEEE, 599--604.Google Scholar
- D. Meredith. 2013. COSIATEC and SIATECCompress: Pattern discovery by geometric compression. In Proceedings of the International Society for Music Information Retrieval Conference.Google Scholar
- N. Mladenović and P. Hansen. 1997. Variable neighborhood search. Comput. Operat. Res. 24, 11 (1997), 1097--1100. Google ScholarDigital Library
- J. A. Moorer. 1972. Music and computer composition. Commun. ACM 15, 2 (1972), 104--113.Google ScholarDigital Library
- R. Morales-Manzanares. 1992. Non-deterministic automatons controlled by rules for composition. In Proceedings of the International Computer Music Conference. 400--400. Google ScholarDigital Library
- R. Morales-Manzanares, E. F. Morales, R. Dannenberg, and J. Berger. 2001. SICIB: An interactive music composition system using body movements. Comput. Music J. 25, 2 (2001), 25--36.Google ScholarDigital Library
- M. C. Mozer. 1991. Connectionist music composition based on melodic, stylistic and psychophysical constraints. Music Connect. (1991), 195--211.Google Scholar
- M. Müller and J. Driedger. 2012. Data-driven sound track generation. In Multimodal Music Processing. 175--194. Google ScholarCross Ref
- J.-I. Nakamura, T. Kaku, K. Hyun, T. Noma, and S. Yoshida. 1994. Automatic background music generation based on actors’ mood and motions. J. Visual. Comput. Animat. 5, 4 (1994), 247--264. Google ScholarCross Ref
- G. Nierhaus. 2009. Algorithmic Composition: Paradigms of Automated Music Generation. Springer.Google ScholarCross Ref
- J. Nika, D. Bouche, J. Bresson, M. Chemillier, and G. Assayag. 2015. Guided improvisation as dynamic calls to an offline model. In Sound and Music Computing (SMC’15).Google Scholar
- H. Norden. 1969. Fundamental Counterpoint. Crescendo Publishing Co., Boston. Google ScholarCross Ref
- F. Pachet. 2003. The continuator: Musical interaction with style. J. New Music Res. 32, 3 (2003), 333--341.Google ScholarCross Ref
- F. Pachet. 2016. A joyful ode to automatic orchestration. ACM Trans. Intell. Syst. Technol. (TIST) 8, 2 (2016), 18.Google Scholar
- F. Pachet, G. Roy, and P. Barbieri. 2001. Finite-length Markov processes with constraints. Transition 6, 1/3 (2001).Google Scholar
- A. Papadopoulos, P. Roy, and F. Pachet. 2014. Avoiding plagiarism in markov sequence generation. In Proceedings of the Innovative Applications of Artificial Intelligence Conference (AAAI’14). Quebec.Google Scholar
- G. Papadopoulos and G. Wiggins. 1999. AI methods for algorithmic composition: A survey, a critical view and future prospects. In Proceedings of the AISB Symposium on Musical Creativity. Edinburgh, UK, 110--117.Google Scholar
- R. Parke, E. Chew, and C. Kyriakakis. 2007. Quantitative and visual analysis of the impact of music on perceived emotion of film. Comput. Entertain. (CIE) 5, 3 (2007), 5. Google ScholarDigital Library
- M. T. Pearce, M. H. Ruiz, S. Kapasi, G. A. Wiggins, and J. Bhattacharya. 2010. Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation. NeuroImage 50, 1 (2010), 302--313.Google ScholarCross Ref
- M. Pearce and G. Wiggins. 2001. Towards a framework for the evaluation of machine compositions. In Proceedings of the Symposium on Artificial Intelligence and Creativity in the Arts and Sciences (AISB’01). 22--32. Google ScholarCross Ref
- I. Peretz, D. Gaudreau, and A.-M. Bonnel. 1998. Exposure effects on music preference and recognition. Memory Cogn. 26, 5 (1998), 884--902.Google ScholarCross Ref
- S. Phon-Amnuaisuk and G. Wiggins. 1999. The four-part harmonisation problem: A comparison between genetic algorithms and a rule-based system. In Proceedings of the Symposium on Musical Creativity (AISB’99). 28--34. Google ScholarCross Ref
- R. C. Pinkerton. 1956. Information theory and melody. Sci. Amer. 194, 2 (1956), 77--86.Google Scholar
- J. Polito, J. Daida, and T. Bersano-Begey. 1997. Musica ex machina: Composing 16th-century counterpoint with genetic programming and symbiosis. In Proceedings of the Conference on Evolutionary Programming VI (Lecture Notes in Computer Science), Vol. 1213. Springer, 113--124.Google Scholar
- A. Prechtl, R. Laney, A. Willis, and R. Samuels. 2014a. Algorithmic music as intelligent game music. In Proceedings of the 50th Annual Convention of the AISB, 1--4 April 2014, London, UK. Google ScholarDigital Library
- A. Prechtl, R. Laney, A. Willis, and R. Samuels. 2014b. Methodological approaches to the evaluation of game music systems. In Proceedings of the 9th Audio Mostly: A Conference on Interaction With Sound. ACM, 26. Google ScholarCross Ref
- J. Pritchett. 1994. The completion of John Cage’s freeman etudes. Perspectives of New Music (1994), 264--270.Google Scholar
- D. Psenicka. 2003. Sporch: An algorithm for orchestration based on spectral analyses of recorded sounds. In Proceedings of International Computer Music Conference (ICMC’03). 184.Google Scholar
- D. Radicioni, L. Anselma, and V. Lombardo. 2004. An algorithm to compute fingering for string instruments. In Proceedings of the National Congress of the Associazione Italiana di Scienze Cognitive, Ivrea, Italy.Google Scholar
- A. Radisavljevic and P. Driessen. 2004. Path difference learning for guitar fingering problem. In Proceedings of the International Computer Music Conference, Vol. 28.Google Scholar
- R. Raines. 2015. Composition in the Digital World: Conversations with 21st Century American Composers. Oxford University Press. 241 pages.Google Scholar
- D. Ralley. 1995. Genetic algorithms as a tool for melodic development. Urbana 101 (1995), 61801. Google ScholarDigital Library
- C. Roig, L. J. Tardón, I. Barbancho, and A. M. Barbancho. 2014. Automatic melody composition based on a probabilistic model of music style and harmonic rules. Knowl.-Based Syst. 71 (2014), 419--434.Google ScholarDigital Library
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams. 1988. Learning representations by back-propagating errors. Cogn. Model. 5, 3 (1988), 1. Google ScholarCross Ref
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams. 1985. Learning Internal Representations by Error Propagation. Technical Report. DTIC Document.Google Scholar
- J. Rutherford and G. Wiggins. 2002. An experiment in the automatic creation of music which has specific emotional content. In Proceedings of the 7th International Conference on music Perception and Cognition. Google ScholarCross Ref
- S. I. Sayegh. 1989. Fingering for string instruments with the optimum path paradigm. Comput. Music J. (1989), 76--84.Google Scholar
- I. Schankler, E. Chew, and A. François. 2014. Improvising with digital auto-scaffolding: How mimi changes and enhances the creative process. In Digital Da Vinci. Springer, 99--125.Google Scholar
- V. Sébastien, H. Ralambondrainy, O. Sébastien, and N. Conruyt. 2012. Score analyzer: Automatically determining scores difficulty level for instrumental e-learning. In Proceedings of Conference of the International Society for Music Information Retrieval (ISMIR’12). 571--576. Google ScholarCross Ref
- R. Siddharthan. 1999. Music, mathematics and bach. Resonance 4, 5 (1999), 61--70. Google ScholarDigital Library
- I. Simon, D. Morris, and S. Basu. 2008. MySong: Automatic accompaniment generation for vocal melodies. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 725--734.Google Scholar
- P. Smolensky. 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. Chapter: Information Processing in dynamical systems: Foundations of harmony theory. MIT Press, Cambridge, MA. 15 (1986), 18. Google ScholarCross Ref
- M. J. Steedman. 1984. A generative grammar for jazz chord sequences. Music Perception (1984), 52--77. Google ScholarDigital Library
- B. L. Sturm. 2006. Adaptive concatenative sound synthesis and its application to micromontage composition. Comput. Music J. 30, 4 (2006), 46--66. Google ScholarCross Ref
- T. Tanaka, B. Bemman, and D. Meredith. 2016. Constraint programming formulation of the problem of generating Milton Babbitt’s all-partition arrays. In Proceedings of the 22nd International Conference on Principles and Practice of Constraint Programming. Toulouse, France. Google ScholarDigital Library
- B. Thom. 2000. BoB: An interactive improvisational music companion. In Proceedings of the 4th International Conference on Autonomous Agents. ACM, 309--316. Google ScholarDigital Library
- A. Tidemann and Y. Demiris. 2008. A drum machine that learns to groove. In KI 2008: Advances in Artificial Intelligence. Springer, 144--151. Google ScholarCross Ref
- P. M. Todd. 1989. A connectionist approach to algorithmic composition. Comput. Music J. (1989), 27--43. Google ScholarCross Ref
- P. Toiviainen. 1995. Modeling the target-note technique of bebop-style jazz improvisation: An artificial neural network approach. Music Perception (1995), 399--413.Google Scholar
- N. Tokui and H. Iba. 2000. Music composition with interactive evolutionary computation. In Proceedings of the 3rd International Conference on Generative Art, Vol. 17:2. 215--226.Google Scholar
- M. W. Towsey, A. R. Brown, S. K. Wright, and J. Diederich. 2001. Towards melodic extension using genetic algorithms. Edu. Technol. Soc. 4, 2 (2001), 54--65.Google Scholar
- C. P. Tsang and M. Aitken. 1999. Harmonizing music as a discipline of constraint logic programming. In Proceedings of the International Computer Music Conference.Google Scholar
- D. R. Tuohy and W. D. Potter. 2005. A genetic algorithm for the automatic generation of playable guitar tablature. In Proceedings of the International Computer Music Conference. 499--502. Google ScholarCross Ref
- P. N. Vassilakis. 2005. An improvisation on the middle-eastern mijwiz; Auditory roughness profiles and tension/release patterns. J. Acoust. Soc. Amer. 117, 4 (2005), 2476--2476. Google ScholarCross Ref
- Rodney Waschka II. 2007. Composing with genetic algorithms: GenDash. In Evolutionary Computer Music. Springer, 117--136. Google ScholarDigital Library
- G. Weinberg and S. Driscoll. 2006. Toward robotic musicianship. Comput. Music J. 30, 4 (2006), 28--45.Google ScholarDigital Library
- I. Xenakis. 1992. Formalized Music: Thought and Mathematics in Composition. Number 6. Pendragon Pr. Google ScholarCross Ref
- J. Yamato, J. Ohya, and K. Ishii. 1992. Recognizing human action in time-sequential images using hidden markov model. In Proceedings of the 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’92). IEEE, 379--385.Google Scholar
- L. Yi and J. Goldsmith. 2007. Automatic generation of four-part harmony. In Proceedings of the BMA, CEUR Workshop.Google Scholar
- R. B. Zajonc. 1968. Attitudinal effects of mere exposure.J. Personal. Soc. Psychol. 9, 2p2 (1968), 1--27.Google ScholarCross Ref
Index Terms
- A Functional Taxonomy of Music Generation Systems
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