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Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
Nicolas Boulanger-Lewandowski, Yoshua Bengio, Pascal Vincent
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.
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| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Music Modeling | JSB Chorales | RNN-NADE | NLL | 5.56 | #5 of 10 | Archive leaderboard | report |
| Music Modeling | JSB Chorales | RNN-RBM | NLL | 6.27 | #6 of 10 | Archive leaderboard | report |
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