  This paper is about creating digital musical instruments where a predictive
neural network model is integrated into the interactive system. Rather than
predicting symbolic music (e.g., MIDI notes), we suggest that predicting future
control data from the user and precise temporal information can lead to new and
interesting interactive possibilities. We propose that a mixture density
recurrent neural network (MDRNN) is an appropriate model for this task. The
predictions can be used to fill-in control data when the user stops performing,
or as a kind of filter on the user's own input. We present an interactive MDRNN
prediction server that allows rapid prototyping of new NIMEs featuring
predictive musical interaction by recording datasets, training MDRNN models,
and experimenting with interaction modes. We illustrate our system with several
example NIMEs applying this idea. Our evaluation shows that real-time
predictive interaction is viable even on single-board computers and that small
models are appropriate for small datasets.
