  In this paper, we learn disentangled representations of timbre and pitch for
musical instrument sounds. We adapt a framework based on variational
autoencoders with Gaussian mixture latent distributions. Specifically, we use
two separate encoders to learn distinct latent spaces for timbre and pitch,
which form Gaussian mixture components representing instrument identity and
pitch, respectively. For reconstruction, latent variables of timbre and pitch
are sampled from corresponding mixture components, and are concatenated as the
input to a decoder. We show the model efficacy by latent space visualization,
and a quantitative analysis indicates the discriminability of these spaces,
even with a limited number of instrument labels for training. The model allows
for controllable synthesis of selected instrument sounds by sampling from the
latent spaces. To evaluate this, we trained instrument and pitch classifiers
using original labeled data. These classifiers achieve high accuracy when
tested on our synthesized sounds, which verifies the model performance of
controllable realistic timbre and pitch synthesis. Our model also enables
timbre transfer between multiple instruments, with a single autoencoder
architecture, which is evaluated by measuring the shift in posterior of
instrument classification. Our in depth evaluation confirms the model ability
to successfully disentangle timbre and pitch.
