Polyphonic Music Generation by Modeling Temporal Dependencies Using a   RNN-DBN
  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. 
Kratarth Goel, Raunaq Vohra, and J.K. Sahoo
cs.LG cs.AI cs.NE