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.
|Title of host publication||Proceedings of the International Conference on New Interfaces for Musical Expression|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||4|
|Publication status||Published - 31 May 2015|
|Event|| International Conference on New Interfaces for Musical Expression - Louisiana, United States|
Duration: 31 May 2015 → 3 Jun 2015
|Conference||International Conference on New Interfaces for Musical Expression|
|Period||31/05/15 → 3/06/15|
- gestural performance
- machine learning
- generative behaviour
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).