An AudioWorklet-based signal engine for a live coding language ecosystem

Bernardo, Francisco, Kiefer, Chris and Magnusson, Thor An AudioWorklet-based signal engine for a live coding language ecosystem. Web Audio Conference, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, 4-6 December 2019. Published in: Proceedings of Web Audio Conference (WAC-2019). (Accepted)

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Abstract

This paper reports on early advances in the design of an ecosystem for creating new live coding languages, optimal for audio synthesis, machine learning and machine listening. We present the design rationale and challenges when applying the Web Audio API, and in particular, an AudioWorklet-based solution to refactoring our digital signal processing library Maximilian.js for our high-performance signal synthesis engine. Furthermore, we contribute with a new system implementation, engineered for modern web applications, and for the live coding community to design their own idiosyncratic languages and interfaces applying our signal engine. The evaluation shows that the system runs with high reliability, efficiency and low latency.

Item Type: Conference Proceedings
Schools and Departments: School of Media, Film and Music > Music
Subjects: Q Science > Q Science (General) > Q0300 Cybernetics > Q0325 Self-organizing systems. Conscious automata > Q0325.5 Machine learning
Q Science > Q Science (General) > Q0300 Cybernetics > Q0325 Self-organizing systems. Conscious automata > Q0334 Artificial intelligence
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Depositing User: Jose Francisco Bernardo
Date Deposited: 10 Oct 2019 12:24
Last Modified: 20 Jan 2020 14:26
URI: http://sro.sussex.ac.uk/id/eprint/86879

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Project NameSussex Project NumberFunderFunder Ref
MIMIC: Musically Intelligent Machines Interacting CreativelyG2434AHRC-ARTS & HUMANITIES RESEARCH COUNCILAH/R002657/1