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.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2014
- DOI:
- 10.48550/arXiv.1412.7927
- arXiv:
- arXiv:1412.7927
- Bibcode:
- 2014arXiv1412.7927G
- Keywords:
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- Computer Science - Machine Learning;
- Computer Science - Artificial Intelligence;
- Computer Science - Neural and Evolutionary Computing
- E-Print:
- 8 pages, A4, 1 figure, 1 table, ICANN 2014 oral presentation. arXiv admin note: text overlap with arXiv:1206.6392 by other authors