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

2012-06-27Code Available0· sign in to hype

Nicolas Boulanger-Lewandowski, Yoshua Bengio, Pascal Vincent

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Abstract

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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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
JSB ChoralesRNN-NADENLL5.56Unverified
JSB ChoralesRNN-RBMNLL6.27Unverified

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