When AI don’t sound like AI: Negotiating aesthetic expectations in technology-mediated musical practice

Abstract This paper examines how aesthetics are constructed in technology-mediated musical practice, focusing on the interplay between cultural expectations of AI-generated sounds and the technical structures determining the behaviour of AI algorithms. Through a reconstruction of events in the Surfing Hyperparameters project, we capture how the sonic aesthetics of the system were constructed by negotiating between our sonic expectations (informed by cultural narratives of ghosts in machines) and the sound produced by the system. We argue that the aesthetics of AI-generated sound are often inspired rather than directly caused by the technology itself. While existing research has identified how tools embed ‘paths of least resistance’ towards certain sonic aesthetics, our work reveals a complementary force: how aesthetic expectations rooted in cultural narratives – from science fiction’s stories of autonomous machines to sonic hauntology’s spectral presences – actively shape design decisions and sonic outcomes. Through a radically transparent approach to documenting mismatches between expectation and reality, we show that the stories practitioners tell while building and making music with technology are performative, constructing rather than merely describing aesthetic realities. Addressing these interplays between imagination, expectation and material reality constitutes an important step towards addressing the complex sociotechnical assemblages in which technology-mediated musical practices come into being.

Authors

Institutions

Publication Details

Journal
Organised Sound
Published
2026-04-14
DOI
https://doi.org/10.1017/s1355771826101228
Primary Topic
Music Technology and Sound Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

When AI don’t sound like AI: Negotiating aesthetic expectations in technology-mediated musical practice

Adam Pultz Melbye, Andrew McPherson, Teresa Pelinski
Organised Sound
Music Technology and Sound Studies
article

When AI don’t sound like AI: Negotiating aesthetic expectations in technology-mediated musical practice

Adam Pultz Melbye, Andrew McPherson, Teresa Pelinski
article en

Abstract

Abstract This paper examines how aesthetics are constructed in technology-mediated musical practice, focusing on the interplay between cultural expectations of AI-generated sounds and the technical structures determining the behaviour of AI algorithms. Through a reconstruction of events in the Surfing Hyperparameters project, we capture how the sonic aesthetics of the system were constructed by negotiating between our sonic expectations (informed by cultural narratives of ghosts in machines) and the sound produced by the system. We argue that the aesthetics of AI-generated sound are often inspired rather than directly caused by the technology itself. While existing research has identified how tools embed ‘paths of least resistance’ towards certain sonic aesthetics, our work reveals a complementary force: how aesthetic expectations rooted in cultural narratives – from science fiction’s stories of autonomous machines to sonic hauntology’s spectral presences – actively shape design decisions and sonic outcomes. Through a radically transparent approach to documenting mismatches between expectation and reality, we show that the stories practitioners tell while building and making music with technology are performative, constructing rather than merely describing aesthetic realities. Addressing these interplays between imagination, expectation and material reality constitutes an important step towards addressing the complex sociotechnical assemblages in which technology-mediated musical practices come into being.

Organised Sound
Queen Mary University of London (GB), Independent Dance (GB), Imperial College London (GB)
Openalex Percentile: Top 16%
Music Technology and Sound Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.