  We present a controllable neural audio synthesizer based on Gaussian Mixture
Variational Autoencoders (GM-VAE), which can generate realistic piano
performances in the audio domain that closely follows temporal conditions of
two essential style features for piano performances: articulation and dynamics.
We demonstrate how the model is able to apply fine-grained style morphing over
the course of synthesizing the audio. This is based on conditions which are
latent variables that can be sampled from the prior or inferred from other
pieces. One of the envisioned use cases is to inspire creative and brand new
interpretations for existing pieces of piano music.
