  While deep generative models have become the leading methods for algorithmic
composition, it remains a challenging problem to control the generation process
because the latent variables of most deep-learning models lack good
interpretability. Inspired by the content-style disentanglement idea, we design
a novel architecture, under the VAE framework, that effectively learns two
interpretable latent factors of polyphonic music: chord and texture. The
current model focuses on learning 8-beat long piano composition segments. We
show that such chord-texture disentanglement provides a controllable generation
pathway leading to a wide spectrum of applications, including compositional
style transfer, texture variation, and accompaniment arrangement. Both
objective and subjective evaluations show that our method achieves a successful
disentanglement and high quality controlled music generation.
