Adversarial Synthesis of Drum Sounds
Recent advancements in generative audio synthesis have allowed for the development of creative tools for generation and
manipulation of audio. In this paper, a strategy is proposed for the
synthesis of drum sounds using generative adversarial networks
(GANs). The system is based on a conditional Wasserstein GAN,
which learns the underlying probability distribution of a dataset
compiled of labeled drum sounds. Labels are used to condition
the system on an integer value that can be used to generate audio
with the desired characteristics. Synthesis is controlled by an input
latent vector that enables continuous exploration and interpolation
of generated waveforms. Additionally we experiment with a training method that progressively learns to generate audio at different
temporal resolutions. We present our results and discuss the benefits of generating audio with GANs along with sound examples
and demonstrations.