Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks
This addresses the need for high-quality sound effects in multimedia applications, but it is incremental as it builds on existing GAN methods for a specific domain.
The paper tackled the problem of synthesizing realistic footstep sound effects by implementing two GAN-based architectures, achieving realism scores comparable to recorded samples.
Footsteps are among the most ubiquitous sound effects in multimedia applications. There is substantial research into understanding the acoustic features and developing synthesis models for footstep sound effects. In this paper, we present a first attempt at adopting neural synthesis for this task. We implemented two GAN-based architectures and compared the results with real recordings as well as six traditional sound synthesis methods. Our architectures reached realism scores as high as recorded samples, showing encouraging results for the task at hand.