Lead Sheet Generation and Arrangement by Conditional Generative Adversarial Network
Abstract
Research on automatic music generation has seen great progress due to the development of deep neural networks. However, the generation of multi-instrument music of arbitrary genres still remains a challenge. Existing research either works on lead sheets or multi-track piano-rolls found in MIDIs, but both musical notations have their limits. In this work, we propose a new task called lead sheet arrangement to avoid such limits. A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs. Our model can generate lead sheets and their arrangements of eight-bar long. Audio samples of the generated result can be found at https://drive.google.com/open?id=1c0FfODTpudmLvuKBbc23VBCgQizY6-Rk
Cite
@article{arxiv.1807.11161,
title = {Lead Sheet Generation and Arrangement by Conditional Generative Adversarial Network},
author = {Hao-Min Liu and Yi-Hsuan Yang},
journal= {arXiv preprint arXiv:1807.11161},
year = {2018}
}
Comments
7 pages, 7 figures and 4 tables