Hello SC community,
I’m sure there are people here who have interesting perspectives on the below!
Erik
Call for Submissions for a Special Issue of Computer Music Journal
Title: New Perspectives on Models for Critical and Creative Sound Practice
Editors: Christopher Haworth (c.p.haworth@bham.ac.uk) and Erik Nyström (Erik.Nystrom@citystgeorges.ac.uk)
Manuscript submission deadline: 12 January 2026
Models can be miniatures, proxies, abstractions, idealisations, theories, and stand-ins for reality (Frigg 2022). At once theoretical and applied, descriptive and predictive, small and large scale, understanding how models both represent and intervene in reality is a growing priority for scholars making sense of power, politics, and selfhood in our increasingly datafied worlds (Weisberg 2012; Mackenzie 2017; Downey & Paglen 2024). Models have always been a matter of concern for computer musicians, and they cross almost all of its domains—sound synthesis, gesture recognition, spatialization, interactive systems, acoustic and perceptual theory. In the twenty-first century, models can appear in areas so broad and ubiquitous as to pass for the ground truth about sound—as with Fourier models (Kromhout 2021, Mills 2022)—and so local and particular as to question the world-model distinction on which they rely—as with the highly-individualised clones and mimics enabled by machine learning-driven signal processing (Mackenzie 2017). To the extent that we ‘know’ modelling in computer music, it tends to be on the disciplinary terms of mathematics and engineering. Here, the desire for realistic simulation—whether of instruments, voices, spaces, performance gestures—is often intrinsically tied to rubrics of efficiency, whether that be technological, monetary, or labour-related (Mills 2012, Sterne 2012). Modelling paradigms that depart from realism, and that are driven by non-political economic exigencies—for instance, creative, critical, or speculative approaches—remain marginal.
In the computer music literature, an enduring counter-trend to modelling-as-simulation has been so-called ‘non-standard’ sound synthesis techniques (Holtzman 1978). Associated with composers like Gottfried M Koenig, Herbert Brün, Paul Berg, and Iannis Xenakis, these empirically-guided, experimental models used machine instructions to generate the sequences of samples to be synthesized, such that there was no ‘superordinate acoustic model’ that the resultant sound can be described by (Holtzmann 1994, 244). Such methods have been celebrated for challenging the ‘dualistic’ sound-structure paradigm that prevails in computer music (Di Scipio 1994). Indeed, they equally can be seen as challenging the division of labour between composer and ‘technician-servicer’ (Born 1995, 204). Yet despite association with 'grassroots resistance’ to ‘the determining logic of technology’ (Dobereiner 2011, 36) the reliance of nonstandard synthesis on literacy in digital signal processing maths has limited its take-up to elites of avant-garde engineer-musicians. Where uses of nonstandard synthesis have departed from this domain, for instance in noise and extreme computer music, the tendency has been to embrace the techniques assound models rather than as spurs to generate new ones (Haworth 2015).
Contemporary data-driven synthesis and signal decomposition toolboxes like RAVE (Caillon and Esling 2021) and Flucoma (Tremblay et al 2021) reopen the question of creative modelling and its relation to practice. Indeed, it would not be an exaggeration to talk of a democratisation of advanced computing methods–a shift that has been underdiscussed in terms of its musical implications (Ibid). One implication of this is the new perspective it places on older ideas about creative modelling. In On Sonic Art, Trevor Wishart observed that computers allow musicians an unprecedented ability to model sound by specifying sonic invariants through programming, meaning that ‘we are not confined to basing our sound-models on existing physical objects and systems. We may build a model of a technologically (or even physically) impossible object’ (Wishart, 1986, 327). How is this process transformed when a-priori (models) and a-posteriori (listening observation) are brought closer to one another? Or are they in fact driven further away, as some recent practices with hyperreal physical models suggest (Mudd 2024)?
But new thinking on modelling need not only be driven by technological factors. We could equally use this juncture to take stock of work that reassesses the boundary between mathematical-technological models and philosophical-aesthetic ones (De Souza 2024), to better understand the tacit, embodied ways practitioners devise and use models of all kinds in their work, including more speculative, philosophical approaches (Nyström 2018).
In this call we ask: what concepts and terminology are adequate to the situation of computer music in the twenty-first century, when modelling is becoming untethered from specialist disciplines and little of what we encounter in our digital lives is immune to simulation? We invite contributions on topics including, but not limited to, the following:
· Can modelling perform critical functions relative to questions of natural, artificial, and the increasing intertwining of these attributes in the anthropocene?
· What ways of working with models are emerging that are poorly accounted for by existing disciplinary divisions?
· On what bases do practitioners who experiment with models appraise the sonic outcomes?
· Where do contemporary practitioners locate the boundary between devising a model and using one?
· Can the use of particular models carry a politics?
· What theoretical resources do we need to understand modelling in contemporary music – can greater attention to modelling impact canonical analytical paradigms like spectromorphology, or do we need new ones?
· Does AI-driven modelling of sound propose a paradigm that could reframe the dualistic source-transformation creative strategy present in much electroacoustic music practice?
· What impact can a democratisation of advanced computing technology have on creative modelling in sound practice?
Submissions should follow all CMJ author guidelines (https://direct.mit.edu/comj/pages/submission-guidelines) except that manuscripts should not be submitted online at cmjdb.com. Instead, submissions and queries should be addressed to guest editors Christopher Haworth and Erik Nyström with subject [CMJ | Critical | Models].
References
Born, G., 1995. Rationalizing culture: IRCAM, Boulez, and the institutionalization of the musical avant-garde. Univ of California Press.
De Souza, J. 2024. “Modular Synthesizers as Conceptual Models”. In Modular Synthesis: Patching Machines and People (eds Teboul, E.J., Kitzmann, A. and Engström, E.) CRC Press.
Di Scipio, A. 1994. “Formal processes of timbre composition challenging the dualistic
paradigm of computer music.‘’ Proceedings of ISEA, from Internet: www.uiah.fi/
bookshop/isea_proc/nextgen/j/19.html.
Döbereiner, L., 2011. “Models of constructed sound: Nonstandard synthesis as an aesthetic perspective”. Computer Music Journal, 35(3), pp.28-39.
Downey, A. and Paglen, T., 2024. Trevor Paglen: Adversarially Evolved Hallucinations. Sternberg Press.
Caillon, A. and Esling, P. 2021., ‘RAVE: A variational autoencoder for fast and high-quality neural audio synthesis’, arXiv.org [Preprint]. Available at: https://www.proquest.com/docview/2595779029?pq-origsite=summon&sourcetype=Working%20Papers (Accessed: 13 June 2024).
Frigg, R., 2022. Models and theories: A philosophical inquiry. Taylor & Francis.
Haworth, C., 2015. “Sound synthesis procedures as texts: An ontological politics in electroacoustic and computer music”. Computer Music Journal, 39(1), pp.41-58.
Holtzman, S. R. 1978. “A Description of an Automatic Digital Sound Synthesis Instrument.” DAI research report No. 59. Edinburgh: Department of Artificial Intelligence.
Holtzman, S.R., 1995. Digital mantras: the languages of abstract and virtual worlds. Mit Press.
Kromhout, M.J., 2021. The Logic of Filtering: How Noise Shapes the Sound of Recorded Music. Oxford University Press.
Mackenzie, A., 2017. Machine learners: Archaeology of a data practice. Mit Press.
Mills, M., 2012. “Media and prosthesis: the vocoder, the artificial larynx, and the history of signal processing”. Qui Parle: Critical Humanities and Social Sciences, 21(1), pp.107-149.
Mills, M. 2022. “Everything Is a Filter: George Campbell and the Development of the Electrical Filter in the Bell System (1903-1915).” CCRMA - Stanford University, April 27. www.youtube.com/watch?v=3ri9vM5n2iI
Mudd, T., 2024. “Speculative physical modelling synthesis: Personal reflections on making music with the NESS brass and guitar models”. In Procedings of the Speculative Sound Synthesis Symposium 2024.
Nyström, 2018, “Topographic Synthesis: Parameter distribution in spatial texture”, Proceedings of ICMC 2018. https://quod.lib.umich.edu/i/icmc/bbp2372.2018.022/–topographic-synthesis-parameter-distribution-in-spatial?view=image
Sterne, J., 2012. MP3: The meaning of a format. Duke University Press.
Tremblay, P.A., Roma, G. and Green, O., 2021. “Enabling programmatic data mining as musicking: the fluid corpus manipulation toolkit”. Computer Music Journal, 45(2), pp.9-23.
Weisberg, M., 2012. Simulation and similarity: Using models to understand the world. Oxford University Press.
Wishart, T., 1996. On sonic art. Routledge.