Caramiaux, Baptiste and Tanaka, Atau. 2013. Machine Learning of Musical Gestures: Principles and Review. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME), pp. 513-518. [Article]

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Abstract or Description

We present an overview of machine learning (ML) techniques
and their application in interactive music and new
digital instrument design. We first provide the non-specialist
reader an introduction to two ML tasks, classification and
regression, that are particularly relevant for gestural interaction.
We then present a review of the literature in current
NIME research that uses ML in musical gesture analysis
and gestural sound control. We describe the ways in which
machine learning is useful for creating expressive musical interaction,
and in turn why live music performance presents
a pertinent and challenging use case for machine learning.

Item Type:	Article
Keywords:	Machine Learning, Data mining, Musical Expression, Musical Gestures, Analysis, Control, Gesture, Sound,
Departments, Centres and Research Units:	Computing > Embodied AudioVisual Interaction Group (EAVI)
Dates:	Date Event May 2013 Published
Item ID:	14645
Date Deposited:	11 Nov 2015 08:13
Last Modified:	29 Apr 2020 16:12
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/14645

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