A Proposal for Foley Sound Synthesis Challenge
This work targets researchers in audio and machine learning by introducing a structured challenge to advance foley synthesis, but it is incremental as it builds on existing challenge frameworks without presenting new methods or results.
The authors proposed a challenge for automatic foley sound synthesis to address the need for standardized evaluation in this growing research area, aiming to foster community participation and rigorous assessment of different systems.
"Foley" refers to sound effects that are added to multimedia during post-production to enhance its perceived acoustic properties, e.g., by simulating the sounds of footsteps, ambient environmental sounds, or visible objects on the screen. While foley is traditionally produced by foley artists, there is increasing interest in automatic or machine-assisted techniques building upon recent advances in sound synthesis and generative models. To foster more participation in this growing research area, we propose a challenge for automatic foley synthesis. Through case studies on successful previous challenges in audio and machine learning, we set the goals of the proposed challenge: rigorous, unified, and efficient evaluation of different foley synthesis systems, with an overarching goal of drawing active participation from the research community. We outline the details and design considerations of a foley sound synthesis challenge, including task definition, dataset requirements, and evaluation criteria.