
RAVE is an audio processing, generative tool, designed by Antoine Caillon and the ACIDS research group at IRCAM. RAVE (Realtime Audio Variational autoEncoder) is a learning framework for generating a neural network model from audio data.
As an encoder, it is able to take sound as an input, and generate new sound as an output – it translates incoming audio into a set of synthesis parameters that are used to generate back the sound.
This is based on two separate processes:
- Encoding process : where a given parameter of inputed audio is transformed into a set a latent variables (128 parameters in general).
- Decoding process: inverts these 128 latent variables back into sound.
As each model is trained on a limited set of data, it will try to extract the parameters even if the input sound does not match the original database. This is why RAVE is able to perform timbre transfer. For example, if RAVE has been trained on piano sounds, and is then given a violin sound, it will try to extract synthesis parameters from it and generate it as a piano sound. This allows you to use RAVE as an audio effect through transforming incoming audio, but also as a synthesiser – by directly controlling these latent parameters.

I discovered RAVE through Michael at the LCC Creative Technology Hub and as soon as he mentioned it I recognised the importance of RAVE for myself in this project. It, itself is an AI tool that artists are using to create generative sonic work, with a strong community surrounding the tool, this is a great example of subcultures forming around AI.
RAVE can be seen as a more ethical AI alternative because it’s architectural design, giving you, the user, control over the data being inputed for the generative output. Artists providing sources are able to consent to the use of their audio, therefore if you train it on your own recordings or licensed datasets, it avoids the ‘theft’ concerns often associated with AI tools using the internet.
Because it is used more as a creative instrument rather than a replacement for human labor, the perspective of it being an AI tool is different to, say, how large corporations use AI as a tool.
RAVE’s ability to ‘timber transfer’ allows it to be used in a way which many see as a natural progression of digital music.
Like all deep learning models, training RAVE requires significant computational power, contributing to carbon emissions and the use of water – a common environmental concern for all generative AI. It is hard to say if or how this would change as I feel I currently do not know enough about the environmental impact of these supercomputers which are powering AI. I fully comprehend and empathise with the refusal to use AI due to the environmental damage it has. However, there are many contributing factors to this destruction of nature, which, in contemporary society is hard to control due to the involvement of advanced technology in our everyday lives. This is an aspect of AI that contributes hugely to the mass rejection and lack of acceptance to the use of AI in art, therefore is a key aspect that I will be researching for this essay.
RAVE is an facet of AI that I am able to cite for both elements of this project, the essay as well as the composition. It is a key development in my research for understanding the core of my essay, whilst also providing me with an inspiring, new tool which I can use to create my composition work.
With the work I have been doing this past year, (involving Pure data) I have learnt a vast amount as it was an aspect of sound arts I had no prior knowledge of, and so I have built my work from the ground up in that respect. However I found I got to this point with my work, where, I didn’t quite know which direction to take it in. There are a number of routes I am able to take with the knowledge I now acquire, and I feel using RAVE is one I feel motivated to continue in.
I feel it is important to understand the importance of AI in the contemporary artistic world as it is not leaving anytime soon, in fact the opposite. AI will continue to evolve at rapid speeds and in ways that get more complex, therefore being able to comprehend it now feels significant.
With the coding knowledge I have picked up from using Pure data (although tiny compared to others) I feel confident in continuing to learn and use code in a creative way. Inputting my own entire database of audio into RAVE to then use to create work feels very exciting to me, and is a continuous project that will really allow me to understand both RAVE itself and how AI learns and uses sources to create generative sounds.
Some examples of artists using RAVE:
Mouja: https://youtu.be/4qbK3cw3E5M?si=ef7GszYk2-AMbBYi
Tenor Boston 2023 – Concert: https://youtu.be/kuxYIYgPrTs?si=_SLhYMd4lHSRHuGC
