# Beat-Based Rhythm Quantization of MIDI Performances

arXiv: https://arxiv.org/abs/2508.19262 · HTML: https://mlsanity.com/paper/2508.19262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [Non-Local Musical Statistics as Guides for Audio-to-Score Piano Transcription](https://mlsanity.com/paper/2008.12710/) (2020)
- [End-to-end Piano Performance-MIDI to Score Conversion with Transformers](https://mlsanity.com/paper/2410.00210/) (2024)

## Abstract (arXiv)

We propose a transformer-based rhythm quantization model that incorporates beat and downbeat information to quantize MIDI performances into metrically-aligned, human-readable scores. We propose a beat-based preprocessing method that transfers score and performance data into a unified token representation. We optimize our model architecture and data representation and train on piano and guitar performances. Our model exceeds state-of-the-art performance based on the MUSTER metric.