Dual-track Music Generation using Deep Learning
This work addresses music generation for classical piano, but it appears incremental as it builds on existing neural network approaches without a major breakthrough.
The authors tackled the problem of generating classical piano music by proposing a dual-track architecture that models the inter-dependency between left-hand and right-hand parts, and their model outperformed other tested methods, including MuseGAN, under two evaluation methods.
Music generation is always interesting in a sense that there is no formalized recipe. In this work, we propose a novel dual-track architecture for generating classical piano music, which is able to model the inter-dependency of left-hand and right-hand piano music. Particularly, we experimented with a lot of different models of neural network as well as different representations of music, and the results show that our proposed model outperforms all other tested methods. Besides, we deployed some special policies for model training and generation, which contributed to the model performance remarkably. Finally, under two evaluation methods, we compared our models with the MuseGAN project and true music.