Real-time interactive sequence generation and control with Recurrent Neural Network ensembles
Abstract
Recurrent Neural Networks (RNN), particularly Long Short Term Memory (LSTM) RNNs, are a popular and very successful method for learning and generating sequences. However, current generative RNN techniques do not allow real-time interactive control of the sequence generation process, thus aren't well suited for live creative expression. We propose a method of real-time continuous control and 'steering' of sequence generation using an ensemble of RNNs and dynamically altering the mixture weights of the models. We demonstrate the method using character based LSTM networks and a gestural interface allowing users to 'conduct' the generation of text.
Cite
@article{arxiv.1612.04687,
title = {Real-time interactive sequence generation and control with Recurrent Neural Network ensembles},
author = {Memo Akten and Mick Grierson},
journal= {arXiv preprint arXiv:1612.04687},
year = {2017}
}
Comments
Demo presentation at NIPS 2016, and poster presentation at the RNN Symposium at NIPS 2016. 7 pages including 1 page references, 1 page appendix, 2 figures