{"ID":5551744,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T10:42:23.705510313Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00777","arxiv_id":"2607.00777","title":"Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition","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.","short_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...","url_abs":"https://arxiv.org/abs/2607.00777","url_pdf":"https://arxiv.org/pdf/2607.00777v1","authors":"[\"Çağrı Eser\"]","published":"2026-07-01T11:04:28Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false,"code_links":[{"ID":613836,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-02T01:54:51.863792489Z","DeletedAt":null,"paper_id":5551744,"paper_url":"https://arxiv.org/abs/2607.00777","paper_title":"Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition","repo_url":"https://github.com/cagries/tipofmyear","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}