The Machine Learning Algorithm as Creative Musical Tool

TL;DR

This paper explores how machine learning algorithms serve as creative tools in music and sonic arts, emphasizing human-computer interaction and the importance of interface design for musical creativity.

Contribution

It introduces a human-centered perspective on machine learning as a musical interface, highlighting how interaction influences usability and creative potential.

Findings

Machine learning enables personalized musical styles.

Interaction design affects usability and creativity.

Algorithms can be viewed as human-computer interfaces.

Abstract

Machine learning is the capacity of a computational system to learn structures from datasets in order to make predictions on newly seen data. Such an approach offers a significant advantage in music scenarios in which musicians can teach the system to learn an idiosyncratic style, or can break the rules to explore the system's capacity in unexpected ways. In this chapter we draw on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. We motivate a new understanding of learning algorithms as human-computer interfaces. We show that, like other interfaces, learning algorithms can be characterised by the ways their affordances intersect with goals of human users. We also argue that the nature of interaction between users and algorithms impacts the usability and usefulness of those algorithms in profound ways. This human-centred view of machine learning motivates our concluding discussion of what it means to employ machine learning as a creative tool.