  In contemporary popular music production, drum sound design is commonly
performed by cumbersome browsing and processing of pre-recorded samples in
sound libraries. One can also use specialized synthesis hardware, typically
controlled through low-level, musically meaningless parameters. Today, the
field of Deep Learning offers methods to control the synthesis process via
learned high-level features and allows generating a wide variety of sounds. In
this paper, we present DrumGAN VST, a plugin for synthesizing drum sounds using
a Generative Adversarial Network. DrumGAN VST operates on 44.1 kHz sample-rate
audio, offers independent and continuous instrument class controls, and
features an encoding neural network that maps sounds into the GAN's latent
space, enabling resynthesis and manipulation of pre-existing drum sounds. We
provide numerous sound examples and a demo of the proposed VST plugin.
