  In this paper we introduce StyleWaveGAN, a style-based drum sound generator
that is a variation of StyleGAN, a state-of-the-art image generator. By
conditioning StyleWaveGAN on both the type of drum and several audio
descriptors, we are able to synthesize waveforms faster than real-time on a GPU
directly in CD quality up to a duration of 1.5s while retaining a considerable
amount of control over the generation. We also introduce an alternative to the
progressive growing of GANs and experimented on the effect of dataset balancing
for generative tasks. The experiments are carried out on an augmented subset of
a publicly available dataset comprised of different drums and cymbals. We
evaluate against two recent drum generators, WaveGAN and NeuroDrum,
demonstrating significantly improved generation quality (measured with the
Frechet Audio Distance) and interesting results with perceptual features.
