Abstract:
In this paper, we offer reflections on the (still-forming) outcomes of a five-year project, situated in a context of artistic research around music technology, that seeks to facilitate the use of machine listening and machine learning techniques for creative coding musicians working in the Max, Pure Data and Supercollider environments. We have developed a suite of software extensions and learning materials, and, unusually, we have included community development efforts in our work. Whilst the project has no doubt differed in aims, methods and knowledge claims to how PPIG researchers may approach these topics, we feel there is significant common interest in a number of the emerging themes. We focus here on continuing attempts, by user-programmers and library programmers alike, to navigate various tensions thrown up by ambitions for the project’s accessibility, community and continuity. Among these tensions are: cross environment legibility vs cross domain legibility between music and data-science vs environment idioms vs (unknown) idiosyncratic working patterns vs quick iterative design vs maintainability and longevity vs low cost of entry vs penetrability
PPIG 2022 - 33rd Annual Workshop
Architecture about Dancing: Creating a Cross Environment, Cross Domain Framework for Creative Coding Musicians