Hostname: page-component-77c89778f8-gq7q9 Total loading time: 0 Render date: 2024-07-22T17:09:29.307Z Has data issue: false hasContentIssue false
  • English
  • Français

Towards Deconstructivist Music: Reconstruction paradoxes, neural networks, concatenative synthesis and automated orchestration in the creative process

Published online by Cambridge University Press:  01 August 2023

Philon Nguyen Open the ORCID record for Philon Nguyen [Opens in a new window]  and
Eldad Tsabary
Philon Nguyen*
Affiliation:
Department of Music, Faculty of Fine Arts, Concordia University, Montreal, Quebec, Canada
Eldad Tsabary
Affiliation:
Department of Music, Faculty of Fine Arts, Concordia University, Montreal, Quebec, Canada
*
Corresponding author email: philon.nguyen@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Since the 1980s, deconstruction has become a popular approach for designing architecture. In music, however, the term has not been absorbed as well by the related literature, with a few exceptions. In this article, ways to find ideological groundings for deconstructivism in music are introduced through the concepts of enchaînement and reconstruction paradoxes. Similar to the Banach–Tarski paradox in mathematics, reconstruction paradoxes occur when reconstructing the parts of a whole no longer yields the same properties as the whole. In music, a reconstruction paradox occurs when a piece constructed from tonal segments no longer yields a perceived tonality. Deconstruction in architecture heavily relies on computer-aided design (CAD) to realise complex ideas. Similarly in music, computer-aided composition (CAC) techniques such as neural networks, concatenative synthesis and automated orchestration are used. In this article, we discuss such tools in the context of this advocated new aesthetics: deconstructivist music.

Type
Article
Information
Organised Sound , First View , pp. 1 - 12
Check for updates
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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

REFERENCES

Assayag, G. and Bloch, G. 2007. Navigating the Oracle: A Heuristic Approach. Proceedings ICMC’07, the International Comp. Music Association, Copenhagen, 405–12.Google Scholar
Assayag, G., Bloch, G., Chemilliera, M., Cont, A. and Dubnov, S. 2006. OMax Brothers: A Dynamic Topology of Agents for Improvization Learning. AMCMM ’06: Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia, New York, 125–32.Google Scholar
Attinello, P. 1992. Signifying Chaos: A Semiotic Analysis of Sylvano Bussotti’s Siciliano . Repercussions 1(2): 84–110.Google Scholar
Audry, S. 2021. Art in the Age of Machine Learning. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Boden, M. 2009. What is Generative Art? Digital Creativity 20(1/2): 21–46.CrossRefGoogle Scholar
Bogue, R. 2014. Scoring the Rhizome: Bussotti’s Musical Diagram. Deleuze Studies 8(4): 470–90.CrossRefGoogle Scholar
Briot, J.-P., Hadjeres, G. and Pachet, F. 2020. Deep Learning Techniques for Music Generation. New York: Springer.CrossRefGoogle Scholar
Carsault, T. 2017. Automatic Chord Extraction and Musical Structure Prediction through Semi-Supervised Learning: Application to Human-Computer Improvisation. Master’s diss., ATIAM, Université Pierre-et-Marie Curie.Google Scholar
Cage, J. 1958. Fontana Mix. London: Peters Edition.Google Scholar
Chernikhov, I. 1931. The Construction of Architectural and Machine Forms (Konstruktsiya Arhitekturnyih i Mashinnyih Form). Leningrad: Izdanie Leningradskogo obschestva arhitektorov.Google Scholar
Chew, E. 2014. Mathematical and Computational Modeling of Tonality: Theory and Applications. Berlin: Springer.CrossRefGoogle Scholar
Cohn, R. 2012. Audacious Euphony: Chromatic Harmony and the Triad’s Second Nature. Oxford: Oxford University Press.CrossRefGoogle Scholar
Corbussen, M. 2002. Deconstruction in Music. The Jacques Derrida – Gerd Zacher Encounter. PhD diss., Erasmus University Rotterdam.Google Scholar
Crestel, L. and Esling, P. 2017. Live Orchestral Piano: A System for real-Time Orchestral Music Generation. 14th Sound and Music Computing Conference 2017, Espoo, Finland.Google Scholar
Dannenberg, R. B. 1993. Software Support for Interactive Multimedia Performance. Journal of New Music Research 22(3): 213–28.Google Scholar
Deleuze, G. 2013. Mille plateaux: Capitalisme et schizophrénie, 2 . Collection Critique. Paris: Les Editions de Minuit.Google Scholar
Dubnov, S., Assayag, G. and El-Yaniv, R. 1998. Universal Classification Applied to Musical Sequences. Proceedings of the International Computer Music Conference, Michigan.Google Scholar
Eco, U. 1965. L’oeuvre ouverte. Paris: Éditions du Seuil.Google Scholar
Edwards, M. 2011. Algorithmic Composition: Computational Thinking in Music. Communications of the ACM 54(7): 58–67.CrossRefGoogle Scholar
Ferneyhough, B. 1981. Lemma-Icon-Epigram. London: Peters Edition.Google Scholar
François Pachet, J. M., Roy, P. and d’Inverno, M. 2013. Reflexive Loopers for Solo Musical Improvisation. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2205–8.Google Scholar
Handelman, E. and Sigler, A. 2012. Automatic Orchestration for Automatic Composition. AIIDE Workshop AAAI Technical Report WS-12-16.Google Scholar
Hidalgo, A. and Ipinza, C. 2016. Cartografiar lo intangible, El Sonido. Ponencia VI Congreso Internacional de Expresión Gráfica, Córdoba, Argentina.Google Scholar
Huron, D. 2016. Voice Leading: The Science behind a Musical Art. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Iddon, M. and Thomas, P. 2020. John Cage’s Concert for Piano and Orchestra. Oxford: Oxford University Press.CrossRefGoogle Scholar
Ji, S., Luo, J. and Yang, X. 2020. A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions, arXiv: 2011.06801 [cs.SD].Google Scholar
Johnson, P. and Wigley, M. 1988. Deconstructivist Architecture. New York: Museum of Modern Art.Google Scholar
Koblyakov, L. 1993. Pierre Boulez: A World of Harmony. Abingdon: Routledge.Google Scholar
Lehman, F. 2018. Hollywood Harmony: Musical Wonder and the Sound of Cinema. Oxford: Oxford University Press.CrossRefGoogle Scholar
Lerdahl, F. 1996. Generative Theory of Tonal Music. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Lewin, D. 1987. Generalized Musical Intervals and Transformations. New York: Yale University Press.Google Scholar
Lewis, G. E. 2000. Too Many Notes: Computers, Complexity and Culture in Voyager. Leonardo Music Journal 10: 33–9.CrossRefGoogle Scholar
Lewis, J. 1998. Creation by Refinement: A Creativity Paradigm for Gradient Descent Learning Networks. IEEE 1988 International Conference on Neural Networks, New York.CrossRefGoogle Scholar
Lewis, J. and Todd, P. M. 1988. A Sequential Network Design for Musical Application. In Touretzky, D., Hinton, G., and Sejnowski, T. (eds.) Proceedings of the Connectionist Models Summer School, 76–84.Google Scholar
Maestre, E., Ramírez, R., Kersten, S. and Serra, X. 2009. Expressive Concatenative Synthesis by Reusing Samples from Real Performance Recordings. Computer Music Journal 33(4): 23–42.CrossRefGoogle Scholar
Magris, C. 2001. Danube. New York: Vintage.Google Scholar
Maresz, Y. 2013. On Computer-Assisted Orchestration. Contemporary Music Review 32(1): 99–109.CrossRefGoogle Scholar
Messiaen, O. 2000. Technique de mon langage musical. Paris: Alphonse Leduc.Google Scholar
Murail, T. 2004. Modèles et artifices. Strasbourg, France: Presses Universitaires de Strasbourg.Google Scholar
Murail, T. 2005. Spectra and Sprites. Contemporary Music Review 24(2/3): 137–47.CrossRefGoogle Scholar
Nika, J., Déguernel, K., Chemla-Romeu-Santos, A. and Vincent, E. 2017. DYCI2 Agents: Merging the Free, Reactive, and Scenario-Based Music Generation Paradigms. International Computer Music Conference Proceedings, Shangai, China.Google Scholar
de Rauglaudre, D. 2017. Formal Proof of Banach-Tarski Paradox. ASDD-AlmaDL 10(1): 37–49.Google Scholar
Roads, C. 1985. Research in Music and Artificial Intelligence. ACM Computing Surveys 17(2): 163–90.CrossRefGoogle Scholar
Schwarz, D. 2004. Data-Driven Concatenative Sound Synthesis. PhD diss., Université de Paris 6 – Pierre et Marie Curie.Google Scholar
Schwarz, D. 2005. Current Research in Concatenative Sound Synthesis. Proceedings of the International Computer Music Conference (ICMC), Barcelona.Google Scholar
Solomos, M. 2015. Iannis Xenakis, la musique électroacoustique: The Electroacoustic Music. Paris: Editions L’Harmattan.Google Scholar
Subotnik, R. R. 1995. Deconstructive Variations: Music and Reason in Western Society. Minneapolis, MN: University of Minnesota Press.Google Scholar
Thom, B. 2000. BoB: An Interactive Improvisational Music Companion. Proceedings of the Fourth International Conference on Autonomous Agents, AGENTS ’00, Barcelona, 309–16.Google Scholar
Tymoczko, D. 2011. A Geometry of Music. Oxford: Oxford University Press.Google Scholar
Vitale, F. 2019. The Last Fortress of Metaphysics: Jacques Derrida and the Deconstruction of Architecture. Albany, NY: State University of New York Press.Google Scholar
Wigley, M. 1993. The Architecture of Deconstruction: Derrida’s Haunt. Cambridge, MA: MIT Press.Google Scholar
Xenakis, I. 1992. Formalized Music: Thought and Mathematics in Composition. Hillsdale, NY: Pendragon Press.Google Scholar
Young, M. 2007. NN Music: Improvising with a ‘Living’ Computer. In Kronland-Martinet, R., Ystad, S., and Jensen, K. (eds.) Computer Music Modeling and Retrieval. Sense of Sounds. Berlin: Springer, 337–50.Google Scholar
Zils, A. and Pachet, F. 2001. Music Mosaicing. Proceedings of the COST G-6 Conference on Digital Audio Effects (DAFX-01), Limerick, Ireland.Google Scholar