Abstract
This chapter introduces the concept of programming using music, also known as tone-based programming (TBP ). There has been much work on using music and sound to debug code, and also as a way of help people with sight problems to use development environments. This chapter, however, focuses on the use of music to actually create program code, or the use of music as program code. The issues and concepts of TBP are introduced by describing the development of the programming language IMUSIC.
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Banik, S., Watanabe, K., Habib, M., & Izumi, K. (2008). Affection based multi-robot team work. In Lecture notes in electrical engineering (Vol. 21, Part VIII, pp. 355–375).
Boccuzzo, S., & Gall, H. (2009). CocoViz with ambient audio software exploration. In ISCE’09: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (pp 571–574). IEEE Computer Society, Washington DC, USA, 2009.
Busnel, R. G., & Classe, A. (1976). Whistled languages. Berlin: Springer.
Chang, M., Wang, G., et al. (2010). Sonification and vizualisation of neural data. In Proceedings of the International Conference on Auditory Display, Washington D.C., June 9–15,
Cohen, J. (1994). Monitoring background activities. In Auditory display: sonification, audification, and auditory interfaces. Boston, MA: Addison-Wesley.
Cooke, D. (1959). The language of music. Oxford, UK: Oxford University Press.
Cox. G. (2013). Speaking code: Coding as aesthetic and political expression. Cambridge: MIT Press.
Csikszentmihalyi, M. (1997). Flow and the psychology of discovery and invention. Harper Perennial.
Friberg, A. (2004). A fuzzy analyzer of emotional expression in music performance and body motion. In Proceedings of Music and Music Science, Stockholm, Sweden.
Gajewski, B. (1902). Grammaire du Solresol, France.
Grant, M., & Leibson, S. (2007). Beyond the valley of the lost processors: Problems, fallacies, and pitfalls in processor design. In Processor design (pp. pp. 27–67). Springer, Netherlands.
Guzdial, M. (1991). Teaching programming with music: An approach to teaching young students about logo. Logo Foundation.
Harvey, B. (1998). Computer science logo style. Cambridge: MIT Press.
Hechmer, A., Tindale, A., & Tzanetakis, G. (2006). LogoRhythms: Introductory audio programming for computer musicians in a functional language paradigm. In Proceedings of 36th ASEE/IEEE Frontiers in Education Conference.
Hsu, C-L, Wang, D. & Jang, J.-S.R. (2011). A trend estimation algorithm for singing pitch detection in musical recordings, In Proceedings of 2011 IEEE International Conference on Acoustics, Speech and Signal Processing.
Izumi, K., Banik, S., & Watanabe, K. (2009). Behavior generation in robots by emotional motivation. In Proceedings of ISlE 2009, Seoul, Korea.
Juslin, P. (2005). From mimesis to catharsis: expression, perception and induction of emotion in music (pp. 85–116). In Music Communication: Oxford University Press.
Juslin, P., & Laukka, P. (2004). Expression, perception, and induction of musical emotion: a review and a questionnaire study of everyday listening. Journal of New Music Research, 33, 216–237.
Kirke, A. (2015). http://cmr.soc.plymouth.ac.uk/alexiskirke/imusic.htm. Last accessed February 5, 2015.
Kirke, A., & Miranda, E. R. (2012a). Guide to computing for expressive music performance. New York, USA: Springer.
Kirke, A., & Miranda, E. (2012b). Pulsed melodic processing—The use of melodies in affective computations for increased processing transparency. In S. Holland, K. Wilkie, P. Mulholland, & A. Seago (Eds.), Music and human-computer interaction. London: Springer.
Kirke, A., & Miranda, E. R. (2012c). Application of pulsed melodic affective processing to stock market algorithmic trading and analysis. Proceedings of 9th International Symposium on Computer Music Modeling and Retrieval—CMMR2010, London (UK)
Kirke, A., & Miranda, E. R. (2014a). Pulsed melodic affective processing: Musical structures for increasing transparency in emotional computation. Simulation, 90(5), 606–622.
Kirke, A., & Miranda, E. R. (2014b). Towards harmonic extensions of pulsed melodic affective processing—Further musical structures for increasing transparency in emotional computation. International Journal of Unconventional Computation, 10(3), 199–217.
Kirke, A., & Miranda, E. (2015). A multi-agent emotional society whose melodies represent its emergent social hierarchy and are generated by agent communications. Journal of Artificial Societies and Social Simulation, 18(2), 16.
Kirke, A., Gentile, O., Visi, F., & Miranda, E. (2014). MIMED—proposal for a programming language at the meeting point between choreography, music and software development. In Proceedings of 9th Conference on Interdisciplinary Musicology.
Lartillot, O., & Toiviainen, P. (2007). MIR in Matlab (II): A Toolbox for musical feature extraction from audio. In Proceedings of 2007 International Conference on Music Information Retrieval, Vienna, Austria.
Lesiuk, T. (2005). The effect of music listening on work performance. Psychology of Music, 33(2), 173–191.
Livingstone, S.R., Muhlberger, R., & Brown, A.R. (2007). Controlling musical emotionality: An affective computational architecture for influencing musical emotions, Digital Creativity, 18(1) 43–53.
Malatesa, L., Karpouzis, K., & Raouzaiou, A. (2009). Affective intelligence: The human face of AI. In Artificial intelligence. Berlin, Heidelberg: Springer.
Maus, F. (1988). Music as drama. In Music theory spectrum (Vol. 10). California: University of California Press.
Meyer, J. (2005). Typology and intelligibility of whistled languages: Approach in linguistics and bioacoustics. PhD Thesis, Lyon University, France.
Miranda, E. (2001). Composing music with computers. Focal Press.
Myers, B. A., & Ko, A. (2005). More natural and open user interface tools. In Proceedings of the Workshop on the Future of User Interface Design Tools, ACM Conference on Human Factors in Computing Systems.
Palmer, C. (1997) Music performance. Annual Review of Psychology, 48, 115–138.
Paulus, J., & Klapuri, A. (2006). Music structure analysis by finding repeated parts. In Proceedings of AMCMM 2006, ACM, New York, USA.
Picard, R. (2003). Affective computing: challenges. International Journal of Human-Computer Studies, 59(1–2), 55–64.
Raman, T. (1996). Emacspeak—A speech interface. In Proceedings of 1996 Computer Human Interaction Conference.
Sánchez, J., & Aguayo, F. (2006). APL: audio programming language for blind learners. Computers helping people with special needs (pp. 1334–1341). Berlin: Springer.
Stefik, A. (2008). On the design of program execution environments for non-sighted computer programmers. PhD thesis, Washington State University.
Stefik, A., Haywood, A., Mansoor, S., Dunda, B., & Garcia, D. (2009). SODBeans. In Proceedings of the 17th International Conference on Program Comprehension.
Vickers, P., & Alty, J. (2003a). Siren songs and swan songs debugging with music. Communications of the ACM, 46(7), 86–93.
Vickers, P., & Alty, J. (2003). Siren songs and swan songs debugging with music. Communications of the ACM, 46(7).
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Kirke, A. (2017). Toward a Musical Programming Language. In: Miranda, E. (eds) Guide to Unconventional Computing for Music. Springer, Cham. https://doi.org/10.1007/978-3-319-49881-2_9
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