Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition

Çağrı Eser

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arXiv · 2607.00777

Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition

Çağrı Eser

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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 supervised probing and nearest-neighbor retrieval. Our results suggest that from-scratch spectrogram models overfit strongly to training performances, while pretrained embeddings provide better top-$k$ results but are sensitive to performer identity, which can be partially reduced with a lightweight contrastive projection. Our findings motivate jazz standard recognition as a useful stress test for music representation models and as a step toward retrieval-based standard identification. Project page: https://github.com/cagries/tipofmyear.

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Çağrı Eser. 2026-07-01. Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition. https://arxiv.org/abs/2607.00777

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