  We propose a system for rendering a symbolic piano performance with flexible
musical expression. It is necessary to actively control musical expression for
creating a new music performance that conveys various emotions or nuances.
However, previous approaches were limited to following the composer's
guidelines of musical expression or dealing with only a part of the musical
attributes. We aim to disentangle the entire musical expression and structural
attribute of piano performance using a conditional VAE framework. It
stochastically generates expressive parameters from latent representations and
given note structures. In addition, we employ self-supervised approaches that
force the latent variables to represent target attributes. Finally, we leverage
a two-step encoder and decoder that learn hierarchical dependency to enhance
the naturalness of the output. Experimental results show that our system can
stably generate performance parameters relevant to the given musical scores,
learn disentangled representations, and control musical attributes
independently of each other.
