An AudioWorklet-based Signal Engine for a Live Coding Language Ecosystem
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.
@inproceedings{2019_40,
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.},
address = {Trondheim, Norway},
author = {Bernardo, Francisco and Kiefer, Chris and Magnusson, Thor},
booktitle = {Proceedings of the International Web Audio Conference},
editor = {Xambó, Anna and Martín, Sara R. and Roma, Gerard},
month = {December},
pages = {77--82},
publisher = {NTNU},
series = {WAC '19},
title = {An AudioWorklet-based Signal Engine for a Live Coding Language Ecosystem},
year = {2019},
ISSN = {2663-5844}
}