arXiv:2607.00777v1 Announce Type: cross Abstract: Recognizing jazz standards from audio is a challenging form of tune-level music retrieval: different performances of the same standard may vary in tempo, key, arrangement, instrumentation, improvisational content, and even whether the head melody is present. We study this problem using a curated subset of the Jazz Trio Database designed for cross-performance standard recognition. We compare a from-scratch trained Harmonic CNN baseline against frozen pretrained music representations from recent music understanding foundation models, using both s
The proliferation of advanced music understanding foundation models necessitates evaluation of their transfer learning capabilities in niche, complex domains like jazz recognition.
Improving AI's ability to interpret and categorize complex, variable audio like jazz indicates progress in generalizable AI perception, with implications for content indexing, recommendation, and creative tools.
This research provides a benchmark for how well current pretrained music embeddings can handle highly variable, improvisational music, highlighting areas for future model development.
- · AI music research community
- · Music streaming services
- · Creative AI developers
- · Traditional music cataloging methods
Pretrained music models show promise for complex audio analysis, but still require domain-specific tuning or architectural improvements for challenging tasks.
Improved recognition of diverse musical forms could enable more sophisticated AI-driven music generation and personalized listening experiences.
The ability to dissect and understand improvisational content could lead to new tools for music education, analysis, and preservation.
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