  Timbre and pitch are the two main perceptual properties of musical sounds.
Depending on the target applications, we sometimes prefer to focus on one of
them, while reducing the effect of the other. Researchers have managed to
hand-craft such timbre-invariant or pitch-invariant features using domain
knowledge and signal processing techniques, but it remains difficult to
disentangle them in the resulting feature representations. Drawing upon
state-of-the-art techniques in representation learning, we propose in this
paper two deep convolutional neural network models for learning disentangled
representation of musical timbre and pitch. Both models use encoders/decoders
and adversarial training to learn music representations, but the second model
additionally uses skip connections to deal with the pitch information. As music
is an art of time, the two models are supervised by frame-level instrument and
pitch labels using a new dataset collected from MuseScore. We compare the
result of the two disentangling models with a new evaluation protocol called
"timbre crossover", which leads to interesting applications in audio-domain
music editing. Via various objective evaluations, we show that the second model
can better change the instrumentation of a multi-instrument music piece without
much affecting the pitch structure. By disentangling timbre and pitch, we
envision that the model can contribute to generating more realistic music audio
as well.
