Striations
During the session Relationships & Networks in Music, moderated by Paul Craenen, research group 2024 member Bjarni Gunnarsson will present and perform the progress of his project Striations.
Conceived as an experiment for dynamically growing networks as a response to an incoming audio stream, Striations is a software, piece and performance created using SuperCollider, the Keras API and Cytoscape.js. The piece is based on an inference process that has been trained using synthetic sound sources mapping to custom data structures that are designed to appear as nodes in a network.
During a performance, a stream of live-coded sonorities is produced that is analyzed and then used to make predictions of suitable nodes. These nodes then form part of a dynamically growing network of short, articulated sequences that form a counterpart to the synthetic sound. ‘Striations’ runs in a web browser where the visual outcomes of the simulations are displayed. The piece reconsiders the relationship between generative activity using the learning and inference processes and that is subsequently ‘interpreted’ by sound processes in SuperCollider, creating a feedback of causes and interaction.
Running the inference process while growing the network becomes the carrier of musical development. The interaction enforces a way of thinking that revolves around the dynamic building of graphs and sonic behaviors. An attitude that considers musical output as something that emerges from an interaction with a trained model. The idea is that evolving processes are set in motion where the trained mappings influence the live-coding activity and where the creator/composer becomes an active observer of the network growth and inference. The output can only sometimes be controlled in detail but is instead interpreted and further processed.
Live-coded synthesis as a mode of composition is what drives many of the possible applications of ‘Striations’. The observer (live-coder) produces rich computer-generated sound where interaction emerges through the inference of the analysis and the treatment of the growing network.
Besides the live-coded synthesis and building of networks, two other important modes of control are supported. Configuring the context (initial states) and interfering (or blocking) the generated output. For all of the interaction modes, both manual commands can be used as well as audio-driven operations. This way of working introduces an operational space within which highly detailed synthesis instructions algorithms interact and clash with blind generators of computational behaviors that are produced from the learning and through the dynamic growth of the networks. The relationship of influence goes in both directions and across different time scales. ‘Striations’ questions the concepts of generative activity, learning, inference, and network growth, through an ongoing reconfiguration and live-coding.
