  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.
