DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks
Abstract
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.
Keywords
Cite
@article{arxiv.2206.14723,
title = {DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks},
author = {Javier Nistal and Cyran Aouameur and Ithan Velarde and Stefan Lattner},
journal= {arXiv preprint arXiv:2206.14723},
year = {2022}
}
Comments
7 pages, 2 figures, 3 tables, ICML2022 Machine Learning for Audio Synthesis (MLAS) Workshop, for sound examples visit https://cslmusicteam.sony.fr/drumgan-vst/