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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Adversarial_Synthesis_of_Drum_Sounds.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) |
Abstract
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) | ||||||
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| 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 |
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| 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 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/13021 |
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