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ARXIV:2606.06615 · MUSIC RETRIEVAL · SUBMITTED 08 JUN · 20:17 UTC · FRESHNESS FRESH
ARXIV:2606.06615MUSIC RETRIEVALSUBMITTED 08 JUN · 20:17 UTCFRESHNESS FRESHNishit Anand · Ashish Seth · Sreyan Ghosh · Dinesh Manocha · Ramani Duraiswami · arXiv
FIGMA is a multi-view contrastive architecture and dataset for fine-grained music retrieval using detailed natural language descriptions.
Opportunity summary
Pain FIGMA is a multi-view contrastive architecture and dataset for fine-grained music retrieval using detailed natural language descriptions.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
FIGMA is a multi-view contrastive architecture and dataset for fine-grained music retrieval using detailed natural language descriptions. When descriptions specify fine-grained musical attributes such as tempo, key, chord progression, or rhythmic structure, existing models…
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,…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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…
Music Retrieval moved forward this cycle; last verified June 2026. Public score 8.0/10. Production flags indicate code availability.
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FIGMA is a multi-view contrastive architecture and dataset for fine-grained music retrieval using detailed natural language descriptions.
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10.48550/arXiv.2606.06615FIGMA is a multi-view contrastive architecture and dataset for fine-grained music retrieval using detailed natural language descriptions.
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, discarding much of the information encoded in detailed prompts. Then, we propose FIGMA (FIne-Grained Music RetrievAl), a multi-view contrastive architecture that addresses this limitation by jointly optimizing global audio-text alignment and frame-level, token-wise alignment. This design enables FIGMA to capture both high-level semantic context and fine-grained musical attributes within a unified representation space. Moreover, we formalize the task of Fine-Grained Music Retrieval and construct Fine-Grained Music Caption dataset (FGMCaps), a large-scale dataset of 380K music-caption pairs for training along with a 10K test set, both annotated with tempo, key, chord progression, beat count, as well as genre and mood. Extensive experiments demonstrate that FIGMA consistently outperforms existing CLAP-based music retrieval models across multiple music retrieval benchmarks, including out-of-domain evaluations, with relative improvements of up to 73.3%.
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PROBLEM
FIGMA is a multi-view contrastive architecture and dataset for fine-grained music retrieval using detailed natural language descriptions. When descriptions specify fine-grained musical attributes such as tempo, key, chord progression, or rhythmic structure, existing models often...
METHOD
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 r...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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, discarding much...
WHY NOW
Music Retrieval moved forward this cycle; last verified June 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
FIGMA is a multi-view contrastive architecture and dataset for fine-grained music retrieval using detailed natural language descriptions. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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, discarding much of the information encoded in detailed prompts. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Music Retrieval moved forward this cycle; last verified June 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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FIGMA is a multi-view contrastive architecture and dataset for fine-grained music retrieval using detailed natural language descriptions.
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Music Retrieval
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