1 research outputs found
Deep Predictive Models in Interactive Music
Musical performance requires prediction to operate instruments, to perform in
groups and to improvise. In this paper, we investigate how a number of digital
musical instruments (DMIs), including two of our own, have applied predictive
machine learning models that assist users by predicting unknown states of
musical processes. We characterise these predictions as focussed within a
musical instrument, at the level of individual performers, and between members
of an ensemble. These models can connect to existing frameworks for DMI design
and have parallels in the cognitive predictions of human musicians.
We discuss how recent advances in deep learning highlight the role of
prediction in DMIs, by allowing data-driven predictive models with a long
memory of past states. The systems we review are used to motivate musical
use-cases where prediction is a necessary component, and to highlight a number
of challenges for DMI designers seeking to apply deep predictive models in
interactive music systems of the future