Artificial Music I, by Cahn Ingold Prelog

Artificial Music I

by Cahn Ingold Prelog

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1.	Euphoric tech house 03:30	buy track
2.	Lo-fi Romany throat singing funk 02:04	buy track
3.	Space-age easy listening with saxophone and steel band 03:16	buy track
4.	Mid-tempo Brazilian 1960s funk concerto 01:59	buy track
5.	Mellow Polish 1950s rocksteady smooth jazz 03:19	buy track
6.	Melancholy Caribbean bossa nova piano 03:14	buy track
7.	Gong bath 01:16	buy track
8.	Abstract modular synth disco 03:39	buy track
9.	Retro Hindustani grime punk 02:09	buy track
10.	Classical chill 05:44	buy track
11.	Hip-hop gagaku 01:32	buy track
12.	Vaporwave 02:28	buy track
13.	Ambient Brazilian funk bossa nova 02:13	buy track
14.	Warm synth pad 08:02	buy track
15.	Cosmic Iranian 1970s choral techno 03:33	buy track
16.	Cheerful jazzy house 01:54	buy track
17.	Travel video blog background music 02:01	buy track
18.	New age hip-hop panpipes 01:29	buy track
19.	Avant-garde cybernetic jazz techno 01:35	buy track
20.	String quartet 04:38	buy track

about

Text-to-image diffusion models are trained to understand key visual concepts with billions of tagged images. It's a similar (but much more rapid) process to how we learn visual concepts. We know what cats are, because we've seen lots of different cats and understand their common features. And if we want to draw a cat, we use our generic knowledge to create a recognisable picture of a cat, but one that is (very likely to be) slightly different from any other cat picture that we've ever seen.

If the diffusion model is trained specifically on spectrograms, however, rather than pictures of cats, fruit, people etc, then something really interesting happens. Spectrograms are visual representations of sound: plots of frequency vs time, so the machine comes to learn that certain keywords have certain spectrographic similarities. It knows what a generic smooth jazz piece 'looks like' in the same way that it knows what a generic dog looks like. You can then prompt the machine to produce new spectrograms in any style you can think of.

All you have to do then is convert these spectrograms into audio, which is possible thanks to some clever code written by other people, and you end up with some excitingly strange new music, produced by a machine that has absolutely no concept of sound, only about images.

Track titles reflect the prompt used to generate the track, many of which were generated randomly from long lists of genres, countries and adjectives.

credits

released April 6, 2023

Spectrograms by Stable Diffusion with the Riffusion 1.0 model.
Processed into audio with chavinlo's Riffusion Manipulation Tools.
Cover image by Stable Diffusion with the Analog Diffusion 1.0 model.

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about

Simon Proffitt Wales, UK

Aware of what the record-buying public want, but neither able nor willing to give it to them.

2018–2024

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