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.

本文提出了一种基于 VAE 的音乐分离模型，用于将和弦和纹理这两个可解释的因子分离出来，从而实现音乐生成过程的可控，在生成音乐时具有广泛的应用，包括作曲风格转换、纹理变化和伴奏编排，并通过客观和主观评价证明了该方法的成功分离和高质量的音乐生成。