During the 2021-2022 academic year I served as a postdoctoral Research Fellow in Creative Coding at the University of Huddersfield CeReNeM working on the project Fluid Corpus Manipulation (“FluCoMa”).
FluCoMa enables techno-fluent musicians to integrate machine listening and machine learning in their creative practice within Max, SuperCollider, and Pure Data. The toolkit offers tools to separate audio into component parts including slicers and spectral decomposition algorithms, audio analysis tools to describe audio components as analytical and statistical representations, data analysis and machine learning algorithms for pattern detection and expressive dataset browsing, and audio morphing and hybridization algorithms for audio remixing, interpolating, and variation-making.
My main focus on the project was developing pedagogical and learning materials, including lesson plans, teaching workshops, developing curricula, and creating content for FluCoMa learning website, learn.flucoma.org. I also lead the thinking on the SuperCollider interface, help files, and SuperCollider-specific tools.
If you have questions about FluCoMa or are curious where to start, please get in touch!