arXiv Machine Learning July 2, 2026 By \c{C}a\u{g}r{\i} Eser

Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

1d ago

What Makes a Good Layer? Assessing the Layer-Wise Intrinsic Properties of Music Foundation Models

arXiv:2608. 14819v1 Announce Type: cross Abstract: Music foundation models are commonly used as frozen audio feature extractors, yet selecting which layer to extract from remains largely heuristic.

By Angelos-Nikolaos Kanatas, Yuexuan Kong, Pablo Alonso-Jim\'enez, Xavier Serra, Dmitry Bogdanov

arXiv AI

Jul 17

Unsupervised Evaluation of Deep Audio Embeddings for Music Structure Analysis

arXiv:2603. 27218v2 Announce Type: replace-cross Abstract: Music Structure Analysis (MSA) aims to uncover the high-level organization of musical pieces.

By Axel Marmoret

rag computer-vision benchmarks

arXiv AI

Jun 8

FIGMA: Towards FIne-Grained Music retrievAl

arXiv:2606. 06615v1 Announce Type: cross Abstract: Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited to coarse semantic queries.

By Nishit Anand, Ashish Seth, Sreyan Ghosh, Dinesh Manocha, Ramani Duraiswami

benchmarks safety

arXiv Machine Learning

Jul 31

Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

arXiv:2607. 27909v1 Announce Type: cross Abstract: Objective evaluation of expressive MIDI piano performances typically relies on attribute statistics such as timing, velocity, and duration of individual notes.

By Dmitrii Gavrilev, Ilya Borovik, Vladimir Viro

rag safety

arXiv Machine Learning

Jun 24

SLEEPING-DISCO 9M: A large-scale pre-training dataset for generative music modeling

arXiv:2506. 14293v4 Announce Type: replace-cross Abstract: We present Sleeping-DISCO 9M, a large-scale pre-training dataset for music and song.

By Tawsif Ahmed, Andrej Radonjic, Gollam Rabby

arXiv Machine Learning

Jul 27

Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition

arXiv:2607. 22413v1 Announce Type: cross Abstract: Sample retrieval tools can help composers find harmonically compatible material, but querying from a fixed reference sample becomes less informative as arrangements evolve and the harmonic context shifts with each musical decision.

By Austin Rockman

rag

Related stories

What Makes a Good Layer? Assessing the Layer-Wise Intrinsic Properties of Music Foundation Models

Unsupervised Evaluation of Deep Audio Embeddings for Music Structure Analysis

FIGMA: Towards FIne-Grained Music retrievAl

Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

SLEEPING-DISCO 9M: A large-scale pre-training dataset for generative music modeling

Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition