  With the advent of data-driven statistical modeling and abundant computing
power, researchers are turning increasingly to deep learning for audio
synthesis. These methods try to model audio signals directly in the time or
frequency domain. In the interest of more flexible control over the generated
sound, it could be more useful to work with a parametric representation of the
signal which corresponds more directly to the musical attributes such as pitch,
dynamics and timbre. We present VaPar Synth - a Variational Parametric
Synthesizer which utilizes a conditional variational autoencoder (CVAE) trained
on a suitable parametric representation. We demonstrate our proposed model's
capabilities via the reconstruction and generation of instrumental tones with
flexible control over their pitch.
