City Research Online

Embodying Spatial Sound Synthesis with AI in Two Compositions for Instruments and 3D Electronics

Einbond, A. ORCID: 0000-0003-1734-6641, Carpentier, T., Schwarz, D. & Bresson, J. (2024). Embodying Spatial Sound Synthesis with AI in Two Compositions for Instruments and 3D Electronics. Computer Music Journal, 46(4), pp. 1-40. doi: 10.1162/comj_a_00664

Abstract

The situated spatial presence of musical instruments has been well studied in the fields of acoustics and music perception research, but so far has not been the focus of Human-AI interaction. We respond critically to this trend by seeking to “re-embody” interactive electronics using data derived from natural acoustic phenomena. Two musical works, composed for human soloist and computer-generated live electronics, are intended to situate the listener in an immersive sonic environment where real and virtual sources blend seamlessly; to do so, we experimented with two contrasting reproduction setups: a surrounding Ambisonic loudspeaker dome, and a compact spherical loudspeaker array for radiation synthesis. A large database of measured radiation patterns of orchestral instruments served as a training set for machine learning models to control spatially rich 3D patterns for electronic sounds. These are exploited during performance in response to live sounds captured with a spherical microphone array and used to train computer improvisation models and trigger corpus-based spatial synthesis. We show how AI techniques are useful to leverage complex, multidimensional, spatial data in the context of computer-assisted composition and human-computer interactive improvisation.

Publication Type: Article
Additional Information: © 2024 Massachusetts Institute of Technology
Subjects: M Music and Books on Music > M Music
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Communication & Creativity > Performing Arts > Music
SWORD Depositor:
[thumbnail of cmj-2023-final.pdf]
Preview
Text - Accepted Version
Download (13MB) | Preview

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login