Machine learning as meta-instrument: Human-machine partnerships shaping expressive instrumental creation

Abstract

In this chapter, I describe how supervised learning algorithms can be used to build new digital musical instruments. Rather than merely serving as methods for inferring mathematical relationships from data, I showhow these algorithms can be understood as valuable design tools that support embodied, real-time, creativepractices. Through this discussion, I argue that the relationship between instrument builders and instrumentcreation tools warrants closer consideration: the affordances of a creation tool shape the musical potential of theinstruments that are built, as well as the experiences and even the creative aims of the human builder. Understanding creation tools as '‘instruments’’ themselves invites us to examine them from perspectives informedby past work on performer-instrument interactions.