Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition
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 supervised probing and nearest-neighbor retrieval. Our results suggest that from-scratch spectrogram models overfit strongly to training performance...
| Authors | Çağrı Eser |
|---|---|
| Primary category | cs.SD |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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