Gestural Electronic Music using Machine Learning as Generative Device

Jan Schacher; Daniel Bisig; Chikashi Miyama

Gestural Electronic Music using Machine Learning as Generative Device

Jan Schacher, Daniel Bisig, Chikashi Miyama

Zurich University of the Arts

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

8 Citations (Scopus)

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 language	English
Title of host publication	Proceedings of the International Conference on New Interfaces for Musical Expression
Publisher	Association for Computing Machinery (ACM)
Pages	347-350
Number of pages	4
ISBN (Print)	978-0-692-49547-6
Publication status	Published - 31 May 2015
Externally published	Yes
Event	International Conference on New Interfaces for Musical Expression - Louisiana, United States Duration: 31 May 2015 → 3 Jun 2015

Conference

Conference	International Conference on New Interfaces for Musical Expression
Country/Territory	United States
City	Louisiana
Period	31/05/15 → 3/06/15

Keywords

gestural performance
machine learning
generative behaviour
interaction

Cite this

Gestural Electronic Music using Machine Learning as Generative Device. / Schacher, Jan; Bisig, Daniel; Miyama, Chikashi.
Proceedings of the International Conference on New Interfaces for Musical Expression. Association for Computing Machinery (ACM), 2015. p. 347-350.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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title = "Gestural Electronic Music using Machine Learning as Generative Device",

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.",

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AB - 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.

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KW - machine learning

KW - generative behaviour

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BT - Proceedings of the International Conference on New Interfaces for Musical Expression

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T2 - International Conference on New Interfaces for Musical Expression

Y2 - 31 May 2015 through 3 June 2015

ER -

Gestural Electronic Music using Machine Learning as Generative Device

Abstract

Conference

Keywords

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Cite this