FluCoMa Plenary: James Bradbury, Finding Things In Stuff

PhD Candidate James Bradbury (University of Huddersfield) joins the third FluCoMa plenary to discuss his use of Python and FluCoMa tools to sift through large audio databases generated by converting raw files into audio. http://www.flucoma.org/ https://jamesbradbury.xyz/ https://github.com/jamesb93/ The third FluCoMa plenary saw the creative coders involved in the early testing and refinement of the FluCoMa toolboxes, as well as two external specialists and the project team, come together for four days of discussion, brainstorming, and critical engagement with the FluCoMa project. The first toolbox, Fluid Decomposition, was released in a celebratory concert where the first cohort of critical users of the tools premiered their work. /// CREDITS Video camera operator: Sam Gillies Edited by Sam Gillies Assisted by Jacob Hart, James Bradbury, and Laurens van der Wee FluCoMa video bumper by Angela Guyton Recorded November 21, 2019, University of Huddersfield /// BIOGRAPHY I am an Australian composer originally from Perth, Western Australia. I enjoy creating musical systems that vivify the behaviours of computers and machines, at times involving other musicians in this process. A primary interest of mine is to embrace the complexity and non-human nature of modern computing as an integral feature of my work’s aesthetic. These creations often assume a role that is more than just that of a facilitator, tending toward a type of composer in and of itself. This intersection of the system and my own intentions is a rich territory of exploration in which new ideas are discovered, proliferated and developed in novel ways. As of 2017, I am a PhD student under the supervision of Alex Harker and Steven Jan at the University of Huddersfield. /// FluCoMa The Fluid Corpus Manipulation project (FluCoMA) instigates new musical ways of exploiting ever-growing banks of sound and gestures within the digital composition process, by bringing breakthroughs of signal decomposition DSP and machine learning to the toolset of techno-fluent computer composers, creative coders and digital artists. These potent algorithms are currently partially available in closed bespoke software, or in laboratories, but not at a suitable level of modularity within the main coding environments used by the creative researchers, namely Max, Pd and SuperCollider, to allow groundbreaking sonic research into a rich unexploited area: the manipulation of large sound corpora. Indeed, with access to, genesis of, and storage of large sound banks now commonplace, novel ways of abstracting and manipulating them are needed to mine their inherent potential. FluCoMa proposes to tackle this issue by empowering techno-fluent aesthetic researchers with a toolset for signal decomposition, and one for machine learning, as well as support material, in order to experiment with new sound and gesture design untapped in large corpora from within their high-level creative coding workflow. Three degrees of manipulations are set to be explored: (1) expressive browsing and descriptor-based taxonomy, (2) remixing, component replacement, and hybridisation by concatenation, and (3) pattern recognition at component level, with interpolating and variation-making potential. These novel manipulations will yield new sounds, new musical ideas, and new approaches to large corpora. As with previous HISS projects, FluCoMa will deliver its findings open source, in the form of software (standalone and extensions) with extensive documentation and examples, as well as the underlying libraries in C++. Moreover, musical works commissioned to challenge these new methodologies will be released, through concerts and plenaries on surrounding subjects, and documented in academic papers. A users forum will also be at the centre of this emerging research community. FluCoMa is based within the Department of Music and Music Technology, with its Centre for Research in New Music, on the Queensgate Campus of the University of Huddersfield. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 725899.