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

arXiv: https://arxiv.org/abs/2507.03482 · HTML: https://mlsanity.com/paper/2507.03482/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2025.

## Built on by (most cited first, 5 of 5)

- [Evaluating Disentangled Representations for Controllable Music Generation](https://mlsanity.com/paper/2602.10058/) (2026)
- [HumMusQA: A Human-written Music Understanding QA Benchmark Dataset](https://mlsanity.com/paper/2603.27877/) (2026)
- [Taste-aware music retrieval from audio embeddings](https://mlsanity.com/paper/2607.03296/) (2026)
- [Linear Complexity Self-Supervised Learning for Music Understanding with Random Quantizer](https://mlsanity.com/paper/2601.09603/) (2026)
- [What Makes a Good Layer? Assessing the Layer-Wise Intrinsic Properties of Music Foundation Models](https://mlsanity.com/paper/2608.14819/) (2026)

## Abstract (arXiv)

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.