用AI自动生成标签，让音效搜索更快更顺手。
Exploring the User Experience of AI-Assisted Sound Searching Systems for Creative Workflows
- 用CLAP模型自动提取音效语义，无需人工标注
- 实测效率提升，用户挫败感降低但脑力负担不变
- 适合音频创作者和人机交互设计者参考
在音频制作中，高效定位合适的音效是重要但困难的任务。当前多数音效搜索系统依赖人工标注的音频标签，不仅耗时且易出错，影响制作效率。随着对比语言-音频预训练（CLAP）模型的发展，我们探索了一种基于CLAP的无注释音效搜索系统（CLAP-UI）。为评估其效果，我们在真实创作流程中与广泛使用的BBC音效库平台进行对比实验，通过专业级任务评估用户表现、认知负荷和满意度。结果显示，CLAP-UI显著提升了工作效率并降低了用户挫败感，同时保持了相近的认知负担。定性反馈也为未来AI辅助音效搜索系统的设计提供了宝贵洞见。
原文摘要 · Abstract (English)
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.
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