DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks

Abstract

Synthetic creation of drum sounds (e.g., in drum machines) is commonly performed using analog or digital synthesis, allowing a musician to sculpt the desired timbre modifying various parameters. Typically, such parameters control low-level features of the sound and often have no musical meaning or perceptual correspondence. With the rise of Deep Learning, data-driven processing of audio emerges as an alternative to traditional signal processing. This new paradigm allows controlling the synthesis process through learned high-level features or by conditioning a model on musically relevant information. In this paper, we apply a Generative Adversarial Network to the task of audio synthesis of drum sounds. By conditioning the model on perceptual features computed with a publicly available feature-extractor, intuitive control is gained over the generation process. The experiments are carried out on a large collection of kick, snare, and cymbal sounds. We show that, compared to a specific prior work based on a U-Net architecture, our approach considerably improves the quality of the generated drum samples, and that the conditional input indeed shapes the perceptual characteristics of the sounds. Also, we provide audio examples and release the code used in our experiments.

8 pages, 1 figure, 3 tables, accepted in Proc. of the 21st International Society for Music Information Retrieval (ISMIR2020)

References in corpus (9)

• WaveNet: A Generative Model for Raw Audio
• MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
• GANSynth: Adversarial Neural Audio Synthesis
• Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks
• MelNet: A Generative Model for Audio in the Frequency Domain
• Jukebox: A Generative Model for Music
• DDSP: Differentiable Digital Signal Processing
• Universal audio synthesizer control with normalizing flows
• Fréchet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms

Cited by in corpus (3)

• Stochastic Restoration of Heavily Compressed Musical Audio using Generative Adversarial Networks
• Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks
• SpecSinGAN: Sound Effect Variation Synthesis Using Single-Image GANs