Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN

  • Kratarth Goel
  • Raunaq Vohra
  • J. K. Sahoo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)


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 a Restricted Boltzmann Machine (RBM). We apply this technique to the task of polyphonic music generation.


Deep architectures recurrent neural networks music generation creative machine learning Deep Belief Networks generative models 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kratarth Goel
    • 1
  • Raunaq Vohra
    • 1
  • J. K. Sahoo
    • 1
  1. 1.Birla Institute of Technology and SciencePilaniIndia

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