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arXiv · 2507.03482

OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction

Pablo Alonso-Jiménez·Pedro Ramoneda·R. Oguz Araz·Andrea Poltronieri·Dmitry Bogdanov
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Abstract

Developing open-source foundation models is essential for advancing research in music audio understanding and ensuring access to powerful, multipurpose representations for music information retrieval. We present OMAR-RQ, a model trained with self-supervision via masked token classification methodologies using a large-scale dataset with over 330,000 hours of music audio. We experiment with different input features and quantization options, and achieve state-of-the-art performance in music tagging, pitch estimation, chord recognition, beat tracking, segmentation, and difficulty estimation among open self-supervised models. We open-source our training and evaluation pipelines and model weights, available at https://github.com/mtg/omar-rq.

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Pablo Alonso-Jiménez, Pedro Ramoneda, R. Oguz Araz, Andrea Poltronieri, Dmitry Bogdanov. 2025-07-04. OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction. https://arxiv.org/abs/2507.03482

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