STYLEWAVEGAN: STYLE-BASED SYNTHESIS OF DRUM SOUNDS WITH EXTENSIVE CONTROLS USING GENERATIVE ADVERSARIAL NETWORKS

Antoine Lavault (1) , Axel Roebel (1) , Matthieu Voiry (2)

1 Analyse et synthèse sonores [Paris]
2 Apeira Technologies

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.

Domaines: Son [cs.SD]

Antoine Lavault, Axel Roebel, Matthieu Voiry. STYLEWAVEGAN: STYLE-BASED SYNTHESIS OF DRUM SOUNDS WITH EXTENSIVE CONTROLS USING GENERATIVE ADVERSARIAL NETWORKS. 19th Sound and Music Computing Conference (SMC 2022), Jun 2022, Saint-Etienne, France. ⟨hal-03693950⟩