Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-77c89778f8-swr86 Total loading time: 0 Render date: 2024-07-19T10:30:34.507Z Has data issue: false hasContentIssue false

4 - Interactive and generative music: a quagmire for the musical analyst

from Part II - Ideas and challenges

Published online by Cambridge University Press:  05 April 2016

By
Michael Young
Edited by
Simon Emmerson  and
Leigh Landy
Simon Emmerson
Affiliation:
De Montfort University, Leicester
Leigh Landy
Affiliation:
De Montfort University, Leicester
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Expanding the Horizon of Electroacoustic Music Analysis , pp. 80 - 101
Publisher: Cambridge University Press
Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ames, Charles. 1987. Automated composition in retrospect 1956–1986. Leonardo 20(2), 169–85.CrossRefGoogle Scholar
Ariza, Christopher. 2009. The interrogator as critic: The Turing Test and the evaluation of generative music systems. Computer Music Journal 33(2), 48–70.CrossRefGoogle Scholar
Arsenault, Linda Marie. 2000. An introduction to Iannis Xenakis’s stochastic music, four algorithmic analyses. http://hdl.handle.net/1807/13842 (accessed 7 April 2013).Google Scholar
Assayag, Gérard and Bloch, Georges. 2007. Navigating the oracle: A heuristic approach. Proceedings of the International Computer Music Conference. Copenhagen: MPublishing, 405–12.Google Scholar
Biles, John A. 2007. Improvizing with genetic algorithms: GenJam. In Miranda, E. R. and Biles, J. A. (eds.) Evolutionary Computer Music. London: Springer-Verlag, 137–69.Google Scholar
Blackwell, Tim and Young, Michael. 2004. Self-organised music. Organised Sound 9(2), 123–36.CrossRefGoogle Scholar
Bulley, James and Jones, Daniel. 2011. Variable 4: A dynamical composition for weather systems. Proceedings of the International Computer Music Conference. Huddersfield: MPublishing, 449–55.Google Scholar
Cage, John. 1961. Composition as Process: II. Indeterminacy. In Silence. Middletown: Wesleyan University Press, 35–40.Google Scholar
Casey, Michael. 2009. Soundspotting: A new kind of process? In Dean, R. (ed.) The Oxford Handbook of Computer Music. New York: Oxford University Press, 421–56.Google Scholar
Collins, Nick. 2008. The analysis of generative music programs. Organised Sound 13(3), 237–48.CrossRefGoogle Scholar
Colton, Simon and Wiggins, Geraint. 2012. Computational creativity: The final frontier. Proceedings of the 20th European Conference on Artificial Intelligence.Google Scholar
Croft, John. 2007. Theses on liveness. Organised Sound, 12(1), 59–66.CrossRefGoogle Scholar
Dobrian, Christopher and Koopelman, Daniel. 2006. The ‘E’ in NIME: Musical Expression with New Computer Interfaces. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME). Paris, 277–82.Google Scholar
Ebcioglu, Kemal. 1993. Expert system for harmonizing four-part chorales. In Schwanauer, S. M. and Levitt, D. A. (eds.) Machine Models of Music. Cambridge, MA: MIT Press, 385–402.Google Scholar
Eco, U. 1989. The Open Work, trans. Cangogni, Anna. Cambridge, MA: Harvard University Press (original Opera aperta, 1962, rev. 1976).Google Scholar
Essl, Karlheinz. 1995. Lexikon-Sonate: An interactive realtime composition for computer-controlled piano. Proceedings of the II Brazilian Symposium on Computer Music. Canela, RS, Brazil.Google Scholar
Harvey, Jonathan. 1999. The metaphysics of live electronics, Contemporary Music Review 18 (3), 79–82.CrossRefGoogle Scholar
Hiller, Lejaren and Isaacson, Leonard. 1993. Musical composition with a high-speed digital computer. In Schwanauer, S. M. and Levitt, D. A. (eds.) Machine Models of Music. Cambridge, MA: MIT Press, 9–22.Google Scholar
Hsu, William and Sosnick, Marc. 2009. Evaluating interactive music systems: An HCI approach. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME). Paris, 25–8.Google Scholar
Kramer, Gregory (ed.). 1994. Auditory Display: Sonification, Audification, and Auditory Interfaces. Reading, MA: Addison-Wesley.Google Scholar
Krefeld, Volker and Waisvisz, Michel. 1990. The hand in the web: An interview with Michel Waisvisz. Computer Music Journal 14(2), 28–33.CrossRefGoogle Scholar
Lewis, George E. 2000. Too many notes: Computers, complexity and culture in Voyager. Leonardo Music Journal 10, 33–9.CrossRefGoogle Scholar
LeWitt, Sol. 1967. Paragraphs on Conceptual Art. Artforum 5(10), 79–83.Google Scholar
Manaris, Bill et al. 2005. Zipf’s law, music classification, and aesthetics. Computer Music Journal 29(1), 55–69.CrossRefGoogle Scholar
May, Andrew. 2006. Philippe Manoury’s Jupiter. In Simoni, M. (ed.) Analytical Methods of Electroacoustic Music. New York: Routledge, 145–86.Google Scholar
Miranda, Eduardo. 2007. Cellular automata music: From sound synthesis to musical forms. In Miranda, E. R. and Biles, J. A. (eds.) Evolutionary Computer Music. London: Springer-Verlag, 170–93.CrossRefGoogle Scholar
Nierhaus, Gerhard. 2009. Algorithmic Composition: Paradigms of Automated Music Generation. Wien: Springer-Verlag.CrossRefGoogle Scholar
Pachet, Francois. 2003. The continuator: Musical interaction with style. Journal of New Music Research 32(3), 333–41.CrossRefGoogle Scholar
Plans Casal, David and Morelli, Davide. 2007. Remembering the future: An overview of co-evolution in musical improvisation. Proceedings of the International Computer Music Conference, 200–5.Google Scholar
Polli, Andrea. 2004. Modelling storms in sound: The Atmospheric/Weather Works Project. Organised Sound 9(2), 175–80.CrossRefGoogle Scholar
Pressing, Jeff. 1987. Improvisation: Methods and models. In Sloboda, J. (ed.) Generative Processes in Music: The Psychology of Performance, Improvisation, and Composition. New York: Oxford University Press, 129–78.Google Scholar
Pressing, Jeff. 1988. Nonlinear maps as generators of musical design. Computer Music Journal 12(2), 35–46.CrossRefGoogle Scholar
Rahn, John. 2004. The swerve and the flow: Music’s relation to mathematics. Perspectives of New Music 42(1), 130–48.Google Scholar
Stowell, Daniel et al. 2009. Evaluation of live human-computer music-making: Quantitative and qualitative approaches. International Journal of Human-Computer Studies 67(11), 960–75.CrossRefGoogle Scholar
Todd, Peter M. and Werner, Gregory M. 1999. Frankensteinian methods for evolutionary music composition. In Griffith, N. and Todd, P. M. (eds.) Musical Networks: Parallel Distributed Perception and Performance. Cambridge, MA: MIT Press, 313–39.Google Scholar
Wanderley, Marcelo and Orio, Nicola. 2002. Evaluation of input devices for musical expression: Borrowing tools from HCI. Computer Music Journal 26(3), 62–76.CrossRefGoogle Scholar
Waschka, Rodney II. 2007. Composing with genetic algorithms: GenDash. In Miranda, E. R. and Biles, J. A. (eds.) Evolutionary Computer Music. London: Springer-Verlag, 117–36.Google Scholar
Worrall, David. 2009. An introduction to data sonification. In Dean, R. (ed.) The Oxford Handbook of Computer Music. New York: Oxford University Press, 312–33.Google Scholar
Young, Michael. 2008. NN Music: Improvising with a ‘living’ computer. In Kronland-Martinet, R., Ystad, S. and Jensen, K. (eds.) Computer Music Modelling and Retrieval: Sense of Sounds. Berlin: Springer-Verlag, 337–50.Google Scholar
Young, Michael. 2010. Identity and intimacy in human-computer improvisation. Leonardo Music Journal [online] 20. www.mitpressjournals.org/doi/abs/10.1162/LMJ_a_00022 (accessed 9 April 2013).CrossRefGoogle Scholar
Young, Michael and Blackwell, Tim. 2013. Live algorithms for music: Can computers be improvisers? In Lewis, G. E. (ed.) The Oxford Handbook of Critical Improvisation Studies. Oxford University Press.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×