Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN
Abstract
In this paper, we propose a generic technique to model temporal dependencies and sequences using a combination of a recurrent neural network and a Deep Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state of the RNN that allows it to provide temporal information and a multi-layer DBN that helps in high level representation of the data. This makes RNN-DBNs ideal for sequence generation. Further, the use of a DBN in conjunction with the RNN makes this model capable of significantly more complex data representation than an RBM. We apply this technique to the task of polyphonic music generation.
Keywords
Cite
@article{arxiv.1412.7927,
title = {Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN},
author = {Kratarth Goel and Raunaq Vohra and J. K. Sahoo},
journal= {arXiv preprint arXiv:1412.7927},
year = {2014}
}
Comments
8 pages, A4, 1 figure, 1 table, ICANN 2014 oral presentation. arXiv admin note: text overlap with arXiv:1206.6392 by other authors