Paper

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

We investigate the problem of modeling symbolic sequences of polyphonic music\nin a completely general piano-roll representation. We introduce a probabilistic\nmodel based on distribution estimators conditioned on a recurrent neural\nnetwork that is able to discover temporal dependencies in high-dimensional\nsequences. Our approach outperforms many traditional models of polyphonic music\non a variety of realistic datasets. We show how our musical language model can\nserve as a symbolic prior to improve the accuracy of polyphonic transcription.\n

Authors: Boulanger-Lewandowski, Nicolas · Bengio, Yoshua · Vincent, Pascal

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