Exploring the User Experience of AI-Assisted Sound Searching Systems for Creative Workflows
Abstract
Locating the right sound effect efficiently is an important yet challenging topic for audio production. Most current sound-searching systems rely on pre-annotated audio labels created by humans, which can be time-consuming to produce and prone to inaccuracies, limiting the efficiency of audio production. Following the recent advancement of contrastive language-audio pre-training (CLAP) models, we explore an alternative CLAP-based sound-searching system (CLAP-UI) that does not rely on human annotations. To evaluate the effectiveness of CLAP-UI, we conducted comparative experiments with a widely used sound effect searching platform, the BBC Sound Effect Library. Our study evaluates user performance, cognitive load, and satisfaction through ecologically valid tasks based on professional sound-searching workflows. Our result shows that CLAP-UI demonstrated significantly enhanced productivity and reduced frustration while maintaining comparable cognitive demands. We also qualitatively analyzed the participants' feedback, which offered valuable perspectives on the design of future AI-assisted sound search systems.
Cite
@article{arxiv.2504.15575,
title = {Exploring the User Experience of AI-Assisted Sound Searching Systems for Creative Workflows},
author = {Haohe Liu and Thomas Deacon and Wenwu Wang and Matt Paradis and Mark D. Plumbley},
journal= {arXiv preprint arXiv:2504.15575},
year = {2025}
}