Adversarial Synthesis of Drum Sounds

Drysdale, Jake and Tomczak, Maciej and Hockman, Jason (2020) Adversarial Synthesis of Drum Sounds. In: Digital Audio Effects 2020, 6th - 10th September 2020.

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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.

Item Type: Conference or Workshop Item (Paper)
1 June 2020Accepted
4 September 2020Published Online
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-04 - software engineering
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Jason Hockman
Date Deposited: 01 Apr 2022 10:42
Last Modified: 22 Mar 2023 12:01

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