Learning Disentangled Representations for Timber and Pitch in Music Audio

Understand

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.