Lead Sheet Generation and Arrangement by Conditional Generative Adversarial Network
Journal
Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
ISBN
9781538668047
Date Issued
2018-07-02
Author(s)
Liu, Hao Min
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. Source code and audio samples of the generated result can be found at the project webpage: https://liuhaumin. github.io/LeadsheetArrangement.
Subjects
Conditional generative adversarial network | Lead sheet arrangement | Multi-track polyphonic music generation
Type
conference paper