3672952
doi
10.5281/zenodo.3672952
oai:zenodo.org:3672952
user-nime_conference
Torresen, Jim
An Interactive Musical Prediction System with Mixture Density Recurrent Neural Networks
Martin, Charles Patrick
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
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.
Zenodo
2019-06-01
info:eu-repo/semantics/conferencePaper
3672951
user-nime_conference
1582053659.17989
2291605
md5:2f530555866a4858c37953129fcb1d64
https://zenodo.org/records/3672952/files/nime2019_paper050.pdf
public
10.5281/zenodo.3672951
isVersionOf
doi
Proceedings of the International Conference on New Interfaces for Musical Expression
260--265
2019-06-01