Algorithms can Mimic Human Piano Performance: The Deep Blues of Music

References

• Baba, T., Hashida, M., & Katayose, H. (2010). “VirtualPhilharmony”: A conducting system with heuristics of conducting an orchestra. In K. Beilharz, A. Johnston, S. Ferguson & A.Y.-C. Chen (Eds.), Proceedings of the 2010 Conference on New Interfaces for Musical Expression (NIME 2010) (pp. 263–270): ACM Press.
   Google Scholar
• Bangert, D., Schubert, E., & Fabian, D. (2014). A spiral model of musical decision-making. Frontiers in Psychology, 5, doi:10.3389/fpsyg.2014.00320
   PubMed Web of Science ®Google Scholar
• BBC_News. (2016). Artificial intelligence: Google’s AlphaGo beats Go master Lee Se-dol. Retrieved October 8, 2016, from http://www.bbc.com/news/technology-35785875
   Google Scholar
• Bisesi, E., Parncutt, R., & Friberg, A. (2011). An accent-based approach to performance rendering: Music theory meets music psychology. In A. Williamon, D. Edwards, & L. Bartel (Eds.), International Symposium on Performance Science (ISPS 2011) (pp. 27–32). Utrecht, The Netherlands: European Association of Conservatoires (AEC).
   Google Scholar
• Bishop, M., & Boden, M. A. (2010). The Turing test and artistic creativity. Kybernetes, 39, 409–413.
   Web of Science ®Google Scholar
• Boden, M. A. (2009). Computer models of creativity. AI Magazine, 30, 23.
   Web of Science ®Google Scholar
• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77–101.10.1191/1478088706qp063oa
   Google Scholar
• Bresin, R., & Friberg, A. (2013). Evaluation of computer systems for expressive music performance. In A. Kirke & E. R. Miranda (Eds.), Guide to computing for expressive music performance (pp. 181–203). London: Springer-Verlag.10.1007/978-1-4471-4123-5
   Google Scholar
• Bringsjord, S., Bello, P., & Ferrucci, D. (2001). Creativity, the Turing test, and the (better) Lovelace test. Minds and Machines, 11, 3–27.10.1023/A:1011206622741
   Web of Science ®Google Scholar
• Canazza, S., De Poli, G., & Rodà, A. (2013). How do people assess computer generated expressive music performances. In R. Bresin (Ed.), Proceedings of the Sound and Music Computing Conference 2013 (SMC 2013), Stockholm, Sweden (pp. 353–359).
   Google Scholar
• Canazza, S., De Poli, G., Rodà, A., & Vidolin, A. (2012). Expressiveness in music performance: Analysis, models, mapping, encoding. In J. Steyn (Ed.), Structuring Music through Markup Language: Designs and Architectures (pp. 156–186). Hershey, PA: IGI Global.
   Google Scholar
• Canazza, S., De Poli, G., Drioli, C., Rodà, A., & Vidolin, A. (2004). Modeling and control of expressiveness in music performance. Proceedings of the IEEE, 92(4), 686–701.
   Web of Science ®Google Scholar
• Canazza, S., De Poli, G., & Rodà, A. (2015). CaRo 2.0: An Interactive System for Expressive Music Rendering. Advances in Human-Computer Interaction, 2015, 1–13. doi: 10.1155/2015/850474
   Web of Science ®Google Scholar
• Clarke, E., & Doffman, M. (2014). Expressive performance in contemporary concert music. In D. Fabian, R. Timmers, & E. Schubert (Eds.), Expressiveness in music performance (pp. 98–114). Oxford: Oxford University Press.10.1093/acprof:oso/9780199659647.001.0001
   Google Scholar
• Cope, D. (2006). Computer models of musical creativity. Cambridge, MA: The MIT Press.
   Google Scholar
• De Poli, G. (2004). Methodologies for expressiveness modelling of and for music performance. Journal of New Music Research, 33, 189–202.10.1080/0929821042000317796
   Web of Science ®Google Scholar
• De Poli, G., Canazza, S., Rodà, A., & Schubert, E. (2015). The role of individual difference in judging expressiveness of computer-assisted music performances by experts. ACM Transactions on Applied Perception, 11(4), 1–20. doi:10.1145/2668124
   Web of Science ®Google Scholar
• Dennett, D. (2001). Collision, detection, muselot, and scribble: Some reflections on creativity. In D. Cope (Ed.), Virtual music, computer synthesis of musical style (pp. 283–291). Cambridge: MIT Press.
   Google Scholar
• Dibben, N. (2014). Understanding performance expression in popular music recordings. In D. Fabian, R. Timmers, & E. Schubert (Eds.), Expressiveness in music performance. Empirical approaches across styles and cultures (pp. 117–132). Oxford: OUP.
   Google Scholar
• Fabian, D., Timmers, R., & Schubert, E. (Eds.). (2014). Expressiveness in music performance—A cross cultural and interdisciplinary approach. Oxford: Oxford University Press.
   Google Scholar
• Friberg, A., & Bisesi, E. (2014). Using computational models of music performance to model stylistic variations. In D. Fabian, R. Timmers, & E. Schubert (Eds.), Expressiveness in music performance (pp. 240–259). Oxford: Oxford University Press.10.1093/acprof:oso/9780199659647.001.0001
   Google Scholar
• Friberg, A., Bresin, R., & Sundberg, J. (2006). Overview of the KTH rule system for musical performance. Advances in Cognitive Psychology, 2, 145–161.10.2478/v10053-008-0052-x
   Google Scholar
• Friberg, A., & Sundberg, J. (1986). A Lisp environment for creating and applying rules for musical performance. In P. Berg (Ed.), Proceedings of International Computer Music Conference, San Francisco, CA (pp. 1–3).
   Google Scholar
• Goebl, W., & Bresin, R. (2003). Measurement and reproduction accuracy of computer-controlled grand pianos. The Journal of the Acoustical Society of America, 114, 2273–2283.10.1121/1.1605387
   PubMed Web of Science ®Google Scholar
• Grachten, M., & Widmer, G. (2012). Linear basis models for prediction and analysis of musical expression. Journal of New Music Research, 41(4), 311–322.
   Web of Science ®Google Scholar
• Hiraga, R., Bresin, R., Hirata, K., & Katayose, H. (2004). Rencon 2004: Turing test for musical expression. Proceedings of the 2004 Conference on New Interfaces for Musical Expression, Hamamatsu, Japan (pp. 120–123). National University of Singapore.
   Google Scholar
• Hiraga, R., Bresin, R., & Katayose, H. (2006). Rencon 2005. Proceedings of the 20th Annual Conference of the Japanese Society for Artificial Intelligence, Funabori, Japan (pp. 1–2, 1D2–1).
   Google Scholar
• Jaffe-Berg, E. (2001). Forays into Grammelot: The Language of Nonsense. Journal of Dramatic Theory and Criticism, 15, 3–15.
   Google Scholar
• Kapur, A. (2005). A history of robotic musical instruments. Proceedings of the International Computer Music Conference, Barcelona, Spain (pp. 21–28).
   Google Scholar
• Kasparov, G. (2010). The chess master and the computer [Book review]. The New York Review of Books, 57, 16–19.
   Web of Science ®Google Scholar
• Katayose, H., Hashida, M., De Poli, G., & Hirata, K. (2012). On evaluating systems for generating expressive music performance: The Rencon experience. Journal of New Music Research, 41, 299–310.10.1080/09298215.2012.745579
   Web of Science ®Google Scholar
• Katayose, H., & Okudaira, K. (2004). Using an expressive performance template in a music conducting interface. Proceedings of New Interfaces for Musical Expression (NIME’04) Conference (pp. 124–129): National University of Singapore.
   Google Scholar
• Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.10.2307/2529310
   PubMed Web of Science ®Google Scholar
• McPherson, G. E., & Thompson, W. F. (1998). Assessing music performance: Issues and influences. Research Studies in Music Education, 10, 12–24.10.1177/1321103X9801000102
   Google Scholar
• van Oosten, P. (1993). Critical study of Sundberg’s rules for expression in the performance of melodies. Contemporary Music Review, 9(1–2), 267–274.
   Google Scholar
• Quinto, L., Thompson, W. F., & Taylor, A. (2014). The contributions of compositional structure and performance expression to the communication of emotion in music. Psychology of Music, 42, 503–524. doi:10.1177/0305735613482023
   Web of Science ®Google Scholar
• Repp, B. H. (1998). Obligatory ‘expectations’ of expressive timing induced by perception of musical structure. Psychological Research, 61, 33–43.10.1007/s004260050011
   PubMed Web of Science ®Google Scholar
• Rodà, A., Schubert, E., De Poli, G., & Canazza, S. (2015). Toward a musical Turing test for automatic music performance. In R. Kronland-Martinet, S. Ystad, & M. Aramaki (Eds.), 11th International Symposium on Computer Music Multidisciplinary Research (CMMR), Plymouth, UK (pp. 697–704).
   Google Scholar
• Schank, R. C., & Abelson, R. P. (1977). Scripts, plans, goals and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum.
   Google Scholar
• Schmuckler, M. A. (2001). What is ecological validity? A dimensional analysis. Infancy, 2, 419–436.
   Google Scholar
• Schubert, E., De Poli, G., Rodà, A., & Canazza, S. (2014). Music Systemisers and Music Empathisers—Do they rate expressiveness of computer generated performances the same? The International Computer Music Conference (ICMC2014) jointly with the Sound and Music Computing (SMC2014), Athens, Greece (pp. 223–227).
   Google Scholar
• Schubert, E., & Fabian, D. (2014). A taxonomy of listeners’ judgments of expressiveness in music performance. In D. Fabian, R. Timmers, & E. Schubert (Eds.), Expressiveness in music performance (pp. 283–303). Oxford: Oxford University Press.10.1093/acprof:oso/9780199659647.001.0001
   Google Scholar
• Schubert, E., & Kreutz, G. (2014). Open ended descriptions of computer assisted interpretations of musical performance: An investigation of individual differences. In A. Rodà & S. Canazza (Eds.), Proceedings of the 1st International Workshop on Computer and Robotic Systems for Automatic Music Performance (SAMP14), Venice, Italy.
   Google Scholar
• Thompson, S., & Williamon, A. (2003). Evaluating evaluation: Musical performance assessment as a research tool. Music Perception, 21, 21–41.10.1525/mp.2003.21.issue-1
   Web of Science ®Google Scholar