Can Machine-Learning Apply to Musical Ensembles?

Date

2016

Authors

Martin, Charles
Gardner, Henry

Journal Title

Journal ISSN

Volume Title

Publisher

Association for Computing Machinery (ACM)

Abstract

In this paper we ask whether machine learning can apply to musical ensembles as well as it does to the individual musical interfaces that are frequently demonstrated at NIME and CHI. While using machine learning to map individual gestures and sensor data to musical output is becoming a major theme of computer music research, these techniques are only rarely applied to ensembles as a whole. We have developed a server-based system that tracks the touch-data of an iPad ensemble and have used such techniques to identify touch-gestures and to characterise ensemble interactions in real-time. We ask whether further analysis of this data can reveal unknown dimensions of collaborative musical interaction and enhance the experience of performers.

Description

Keywords

machine learning, music, ensemble performance, collaborative creativity, ensemble director agent

Citation

Source

Can Machine-Learning Apply to Musical Ensembles?

Type

Conference paper

Book Title

Entity type

Access Statement

Free Access via publisher website

License Rights

Restricted until

2099-12-31