@techreport{TR-IC-PFG-18-16,
   number = {IC-PFG-18-16},
   author  =  {Vitor  Alves Arrais de Souza and Sandra Eliza Fontes de
                   Avila},
   title = {{Deep Neural Networks for Generating Music}},
   month = {July},
   year = {2018},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 15 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Deep  learning  has  been  used  in  many applications to solve
       real-world  problems.  In  recent years, it has seen tremendous
       growth  in  its popularity and usefulness, due in large part to
       more  powerful  computers,  larger  datasets, and techniques to
       train  deeper  networks.  The  objective  of this project is to
       explore  deep learning regarding the field of music composition
       using  artificial  intelligence.  We  deepen a discussion about
       Recurrent Neural networks (RNNs), a type of neural network that
       has  demonstrated  the best results in music generation so far.
       After  that,  we  use  a  Tied  Parallel  Network,  which  is a
       combination of a recurrent and a feedforward network. We modify
       the model to generate songs in such a manner that the rhythm is
       controlled  accordingly  to  the speed of a person. Finally, we
       create  a  system  to  use the model in a real application. The
       system  consists of a trained model capable of generating music
       indefinitely,  an  MP3 streaming server and an Android app that
       tracks the speed by GPS.
  }
}