ML Anthology

Modelling High-Dimensional Sequences with LSTM-RTRBM: Application to Polyphonic Music Generation

Qi Lyu, Zhiyong Wu, Jun Zhu, Helen Meng
IJCAI 2015 pp. 4138-4139
/ijcai/2015/lyu2015ijcai-modelling/

Abstract

We propose an automatic music generation demo based on artificial neural networks, which integrates the ability of Long Short-Term Memory (LSTM) in memorizing and retrieving useful history information, together with the advantage of Restricted Boltzmann Machine (RBM) in high dimensional data modelling. Our model can generalize to different musical styles and generate polyphonic music better than previous models.

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Cite

Text

Lyu et al. "Modelling High-Dimensional Sequences with LSTM-RTRBM: Application to Polyphonic Music Generation." International Joint Conference on Artificial Intelligence, 2015.

Markdown

[Lyu et al. "Modelling High-Dimensional Sequences with LSTM-RTRBM: Application to Polyphonic Music Generation." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/lyu2015ijcai-modelling/)

BibTeX

@inproceedings{lyu2015ijcai-modelling,
  title     = {{Modelling High-Dimensional Sequences with LSTM-RTRBM: Application to Polyphonic Music Generation}},
  author    = {Lyu, Qi and Wu, Zhiyong and Zhu, Jun and Meng, Helen},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {4138-4139},
  url       = {https://mlanthology.org/ijcai/2015/lyu2015ijcai-modelling/}
}