The agent designer toolkit

Authors:
Aengus Martin;Oliver Bown
Affiliations:
The University of Sydney, New South Wales, Australia;The University of Sydney, New South Wales, Australia
Venue:
Proceedings of the 9th ACM Conference on Creativity & Cognition
Year:
2013

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Computers and musical style

Computers and musical style
A toolkit for designing interactive musical agents

Proceedings of the 23rd Australian Computer-Human Interaction Conference

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Abstract

The Agent Designer Toolkit is the result of a study of methods for designing the behaviour of musical agents (i.e. autonomous systems) intended to perform high-level musical decision-making. It uses machine learning methods informed by a musician's knowledge and insights, to discover the salient musical patterns demonstrated in a set of example performances. Based on these patterns, it can produce agents with a variety of behaviours, corresponding to differing degrees of similarity to the demonstrated performance style. The agents can perform in real-time and respond to other musicians or external factors.