  The variational auto-encoder has become a leading framework for symbolic
music generation, and a popular research direction is to study how to
effectively control the generation process. A straightforward way is to control
a model using different conditions during inference. However, in music
practice, conditions are usually sequential (rather than simple categorical
labels), involving rich information that overlaps with the learned
representation. Consequently, the decoder gets confused about whether to
"listen to" the latent representation or the condition, and sometimes just
ignores the condition. To solve this problem, we leverage domain adversarial
training to disentangle the representation from condition cues for better
control. Specifically, we propose a condition corruption objective that uses
the representation to denoise a corrupted condition. Minimized by a
discriminator and maximized by the VAE encoder, this objective adversarially
induces a condition-invariant representation. In this paper, we focus on the
task of melody harmonization to illustrate our idea, while our methodology can
be generalized to other controllable generative tasks. Demos and experiments
show that our methodology facilitates not only condition-invariant
representation learning but also higher-quality controllability compared to
baselines.
