Gestural Electronic Music using Machine Learning as Generative Device

Jan Schacher, Daniel Bisig, Chikashi Miyama

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

When performing with gestural devices in combination with machine learning techniques, a mode of high-level interaction can be achieved. The methods of machine learning and pattern recognition can be re-appropriated to serve as a generative principle. The goal is not classification but reaction from the system in an interactive and autonomous manner. This investigation looks at how machine learning algorithms fit generative purposes and what independent behaviours they enable. To this end we describe artistic and technical developments made to leverage existing machine learning algorithms as generative devices and discuss their relevance to the field of gestural interaction.
Original languageEnglish
Title of host publicationProceedings of the International Conference on New Interfaces for Musical Expression
PublisherAssociation for Computing Machinery (ACM)
Pages347-350
Number of pages4
ISBN (Print)978-0-692-49547-6
Publication statusPublished - 31 May 2015
Externally publishedYes
Event International Conference on New Interfaces for Musical Expression - Louisiana, United States
Duration: 31 May 20153 Jun 2015

Conference

Conference International Conference on New Interfaces for Musical Expression
CountryUnited States
CityLouisiana
Period31/05/153/06/15

Keywords

  • gestural performance
  • machine learning
  • generative behaviour
  • interaction

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  • Cite this

    Schacher, J., Bisig, D., & Miyama, C. (2015). Gestural Electronic Music using Machine Learning as Generative Device. In Proceedings of the International Conference on New Interfaces for Musical Expression (pp. 347-350). Association for Computing Machinery (ACM).