  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.
