Plecto: Investigating the Musical Affordances of Continuous Time Recurrent Neural Networks

"Plecto: Investigating the musical affordances of Continuous Time Recurrent Neural Networks" is a practice-based research project that investigates how continuous time recurrent neural networks (CTRNNs) can be applied to the problem of achieving gestural control in improvised electronic music. One of the challenges of improvising using computers is manipulating different compositional layers during a performance while maintaining granular and expressive control. Artists turn to concepts such as artificial life to solve this problem and pursue software agents with complex, responsive and organic qualities that lead to the perception of lifelikeness. Guided by this theme, I propose a design for a low frequency oscillator (LFO), called Plecto, for use within existing composition workflows that harnesses the idiosyncratic behaviours of CTRNNs as a gestural agent within improvised electronic music performances. CTRNNs have been used in studies of biological modelling such as animal locomotion, and also of minimally cognitive behaviours such as basic object perception. Their ability to produce lifelike abstract forms makes them well suited as a source of gestural control. Oliver Bown and Sebastian Lexer have applied CTRNNs to musical event generation, using evolutionary algorithms (EA) to search for different CTRNN behaviours. I have extended this approach, using a novelty search (NS) variant for the open-ended discovery of CTRNN configurations, each exhibiting novel behaviours that can be applied to different musical problems. Through a series of computational studies, I have explored the lifelike qualities of CTRNNs best suited for gestural control and a novelty search algorithm design for their discovery. An iterative design process was also undertaken, establishing clear design principles adopted to build a usable representation of the CTRNN algorithm within an LFO device built for the Ableton Live environment. Evaluation of the tool was conducted through a user survey and practice-based case studies that incorporate the device into my own improvised electronic music workflow as a gestural agent. The primary outcomes of this research are a suite of software that can be adopted by the broader community of practitioners and a series of compositions reflecting the impacts of the CTRNN algorithm on my creative process