Reproduce “Exploring the User Experience of AI-Assisted Sound Searching Systems for Creative Workflows” on OpenResearch
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About this paper
Haohe Liu, Thomas Deacon, Wenwu Wang, Matt Paradis, Mark D. Plumbley
This study evaluated a CLAP-based AI system for sound searching against a traditional word-matching system within professional creative workflows. The AI-assisted system significantly reduced user frustration and improved perceived performance in finding relevant sound effects, particularly for complex queries, while maintaining a comparable cognitive load.
Key insights
- AI-assisted semantic search systems, built on models like CLAP, can significantly enhance the relevance of sound effect retrieval compared to traditional word-matching methods.
- Natural language querying and multimodal input features, such as searching similar sounds from an uploaded file, greatly improve creative flexibility and search efficiency for audio professionals.
- While offering performance benefits, AI systems for creative workflows still need improvements in areas like integrating high-fidelity audio datasets, providing descriptive metadata, and better handling of complex, multi-element natural language queries.
Summary by alphaXiv.
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