In Reykjavík Sunburn, four different neural audio models, trained on my own musical material — a corpus of electronic music conventionally written and produced — and a private voice dataset are employed in an improvisational setting inside Pure Data, a visual audio programming environment where I perform Latent Jamming, a real-time improvisation practice with neural audio models that embraces concepts of algorithmic and generative composition techniques. I act in real-time inside the models’ latent space, steering mood, density, and rhythmicality by exploring parameter settings in signal streams that resemble latent embeddings. By doing so, I aim to replace deterministic composition with guided exploration: tweak, listen, stabilize, vary.
Reykjavík Sunburn concludes that neural audio synthesis can extend creative practices in music performance and composition by leveraging the unpredictable behaviour of the models from a control interface that caters for creative intent.
In Reykjavík Sunburn each two RAVE and vschaos2 models are being used:
- Black Latents: a RAVE V2 model trained on the Black Plastics series – 28 tracks/ 3h of drum- and percussion-heavy electronic music. The resulting model generates mainly percussive output with rough textures and a generally high grittiness. In the framework, this model is used as a leading asset to generate the rhythmic baseline and general percussive structure.
- Nobsparse: a RAVE V2 model trained on a hybrid dataset of Tech House and sonically sparse Drum & Bass (about 4h of audio material). The model’s characteristics are relatively clear, sterile, and lightweight sounds, harmonic textures, and an isolated but dominant low end. Depending on the process development during the improvisation, this model serves as a secondary texture generator but can also replace Black Latent’s role in the composition.
- VSC2_Nobsparse: this vschaos2 model has been trained on the same dataset as the Nobsparse RAVE model. In the composition, this model is used to generate interchanging pads and drone-like noise textures for transitions or simply to enrich an ongoing section of the recording with a harmonic layer.
- VSC2_Martha2023: being the only model trained on voice data, courtesy of my daughter, this model adds a layer of rhythmical, pseudo-vocal sound on top of the otherwise „instrumental“ generations of the three other models.
Output examples
Reykjavík Sunburn (Take 1 Redux) received recognition at the AI Song Contest 2025 where it was selected to the finalist shortlist of 10 out of >150 submissions.