Modeling Temporal Dependencies in High-Dimensional Sequences:   Application to Polyphonic Music Generation and Transcription
  We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of polyphonic transcription. 
Nicolas Boulanger-Lewandowski (Universite de Montreal), Yoshua Bengio   (Universite de Montreal), Pascal Vincent (Universite...
cs.LG cs.SD stat.ML