DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks
This addresses the problem of inefficient drum sound design for music producers by offering a more intuitive tool, though it is incremental as it applies existing GAN methods to a specific domain.
The paper tackles the cumbersome process of drum sound design in music production by introducing DrumGAN VST, a plugin that uses a Generative Adversarial Network to synthesize drum sounds with high-level controls, enabling resynthesis and manipulation of existing sounds at 44.1 kHz sample-rate audio.
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.