# Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition

arXiv: https://arxiv.org/abs/2607.00777 · HTML: https://mlsanity.com/paper/2607.00777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization](https://mlsanity.com/paper/2501.01108/) (2025)
- [Supervised and Unsupervised Learning of Audio Representations for Music Understanding](https://mlsanity.com/paper/2210.03799/) (2022)
- [Universal Music Representations? Evaluating Foundation Models on World Music Corpora](https://mlsanity.com/paper/2506.17055/) (2025)
- [DisCover: Disentangled Music Representation Learning for Cover Song Identification](https://mlsanity.com/paper/2307.09775/) (2023)

## Abstract (arXiv)

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.