Gestural Electronic Music using Machine Learning as Generative Device

Jan C. Schacher, Chikashi Miyama, Daniel Bisig

In: Proceedings of the International Conference on New Interfaces for Musical Expression NIME’15, May 31-June 3, 2015, Louisiana State Univ., Baton Rouge, LA.

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.

download: pdf  |  bib-reference

MGM_workbench_schema  ML_generative_behaviour MLWorkbench trombone_combined

@inproceedings{Schacher_2015b,
    Address = {Baton Rouge, USA},
    Author = {Jan C. Schacher},
    Booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression, NIME'15,},
    Month = {May 31--June 3},
    Publisher = {Louisiana State University},
    Title = {Gestural Electronic Music using Machine Learning as Generative Device},
    Year = {2015}}