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.

Cet article explore les excursions dans le temps et leurs implications.

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