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
Code Available — Be the first to reproduce this paper.
ReproduceCode
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| JSB Chorales | RNN-NADE | NLL | 5.56 | — | Unverified |
| JSB Chorales | RNN-RBM | NLL | 6.27 | — | Unverified |