C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Artificial Intelligence
2016-12-01 v1 Machine Learning
Abstract
Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.
Cite
@article{arxiv.1611.09904,
title = {C-RNN-GAN: Continuous recurrent neural networks with adversarial training},
author = {Olof Mogren},
journal= {arXiv preprint arXiv:1611.09904},
year = {2016}
}
Comments
Accepted to Constructive Machine Learning Workshop (CML) at NIPS 2016 in Barcelona, Spain, December 10