Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription

Understand

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.