This project is generally focused on integrating Machine Learning techniques to the design, creation, and performative aspect of Digital Musical Instruments (DMIs).
More specifically we are interested in how Generative Machine Learning can be used as a creative tool for musicians irrespective of musical techniques, genres, or performance style. Therefore, the design and implementation of support tools that leverage these technologies is critical to this project.
While ML takes a focal point on this project, we also contribute to the development of open source mapping middleware for establishing and maintaining data signals for multimedia systems. Please take a look at the libmapper project page for more information about that aspect of our larger project.
Current Researchers
Current Publications
We aim to actively share our work with the larger research community. Here is a selected list of our publications to date.
Maplet: Integrating Distributed Data Signal Mappings for Performative Interactions Within the Eurorack Modular Synthesizer Ecosystem Proceedings Article
In: Proceedings of the International Audio Mostly Conference, Milan, Italy, 2024.
Creating Latent Representations of Synthesizer Patches using Variational Autoencoders Proceedings Article
In: Proceedings of the 4th International Symposium on the Internet of Sounds (IS2), Pisa, Italy, 2023.
FAUSTMapper: Facilitating Complex Mappings for Smart Musical Instruments Best Paper Proceedings Article
In: Proceedings of the 4th International Symposium on the Internet of Sounds (IS2), Pisa, Italy, 2023.