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Debris: Machine learning, archive archaeology, digital audio waste

Published online by Cambridge University Press:  30 June 2023

Roberto Alonso Trillo
Affiliation:
Augmented Creativity Lab, Hong Kong Baptist University, Hong Kong. Email: robertoalonso@hkbu.edu.hk
Marek Poliks
Affiliation:
Independent Artist & Researcher, Minneapolis, MN, USA. Email: mpoliks@gmail.com
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Abstract

This article fragments and processes Debris, a project developed to formalise the creative recycling of digital audio byproducts. Debris began as an open call for electronic compositions that take as their point of departure gigabytes of audio material generated through training and calibrating Demiurge, an audio synthesis platform driven by machine learning. The Debris project led us down rabbitholes of structural analysis: what does it mean to work with digital waste, how is it qualified, and what new relationships and methodologies do this foment? To chart the fluid boundaries of Debris and pin down its underlying conceptualisation of sound, this article introduces a framework ranging from archaeomusicology to intertextuality, from actor-network theory to Deleuzian assemblage, from Adornian constellation to swarm intelligence to platform and network topology. This diversity of approaches traces connective frictions that may allow us to understand, from the perspective of Debris, what working with sound means under the regime of machine intelligence. How has machine intelligence fundamentally altered the already shaky diagram connecting humans, creativity and history? We advise the reader to approach the text as a multisensory experience, listening to Debris while navigating the circuitous theoretical alleys below.

Information

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press
Figure 0

Figure 1. Abstractions of 1368614 files as distributed in Demiurge’s GDrive (generated via Folderstats and Graphia).

Figure 1

Table 1. Processing and modelling techniques

Figure 2

Figure 2. Gerard Genette’s transtextual relationships.

Figure 3

Figure 3. Serge Lacasse’s transphonographic relationships.

Figure 4

Figure 4. Mel-spectrogram (lossy resampled representations of audio data used in Demiurge).

Figure 5

Figure 5. MFCC (lossy resampled representations of audio data used in Demiurge).

Figure 6

Figure 6. Ghosts in a vector field.

Figure 7

Figure 7. A 2D representation of a techno-ideological platform.