Yang, Z.S., Hockman, J., 2023.
A plugin for neural audio synthesis of impact sound effects
| Output Type: | Conference paper |
| Presented at: | AM '23: Audio Mostly 2023 |
| Publication: | AM '23: Proceedings of the 18th International Audio Mostly Conference |
| Venue: | Edinburgh, United Kingdom |
| Publisher: | Association for Computing Machinery (ACM), New York |
| Dates: | 30/8/2023 - 1/9/2023 |
| ISBN/ISSN: | 9798400708183 |
| URL: | doi.org/10.1145/3616195.3616221 |
| Pagination: | pp. 143-146 |
The term impact sound as referred to in this paper, can be broadly defined as the sudden burst of short-lasting impulsive noise generated by the collision of two objects. This type of sound effect is prevalent in multimedia productions. However, conventional methods of sourcing these materials are often costly in time and resources. This paper explores the potential of neural audio synthesis for generating realistic impact sound effects, targeted for use in multimedia such as films, games, and AR/VR. The designed system uses a Realtime Audio Variational autoEncoder (RAVE) [2] model trained on a dataset of over 3,000 impact sound samples for inference in a Digital Audio Workstation (DAW), with latent representations exposed as user controls. The performance of the trained model is assessed using various objective evaluation metrics, revealing both the prospects and limitations of this approach. The results and contributions of this paper are discussed, with audio examples and source code made available.