arXiv:2606.06615v1 Announce Type: cross Abstract: Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited to coarse semantic queries. When descriptions specify fine-grained musical attributes such as tempo, key, chord progression, or rhythmic structure, existing models often fail to retrieve the correct audio. We show that this limitation stems from the contrastive learning objective itself: despite being trained on long captions, CLAP-based models effectively utilize only the first few tokens, discar
The continuous development in AI and machine learning, particularly in multi-modal models, drives ongoing research into improving fine-grained understanding and generation, leading to iterative advancements like FIGMA.
This development improves music retrieval accuracy based on detailed natural language descriptions, enabling more precise creative workflows and enhanced user experiences in music-related applications.
Existing audio-text models like CLAP are shown to have limitations in fine-grained musical retrieval, prompting a new approach that better targets specific musical attributes beyond coarse semantics.
- · Music streaming services
- · Music producers/composers
- · AI researchers in audio processing
- · Developers of creative AI tools
- · Current large language models with limited audio-text integration
- · Generic search engines for music
Improved tools for musicologists and artists to categorize and discover music based on complex musical characteristics.
New business models emerging from highly personalized music discovery and creation tools.
The democratization of music composition and production becomes more accessible, impacting the existing music industry structure.
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Read at arXiv cs.LG