# Just Label the Repeats for In-The-Wild Audio-to-Score Alignment

arXiv: https://arxiv.org/abs/2411.07428 · HTML: https://mlsanity.com/paper/2411.07428/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [MT3: Multi-Task Multitrack Music Transcription](https://mlsanity.com/paper/2111.03017/) (2021)
- [madmom: a new Python Audio and Music Signal Processing Library](https://mlsanity.com/paper/1605.07008/) (2016)
- [Unaligned Supervision For Automatic Music Transcription in The Wild](https://mlsanity.com/paper/2204.13668/) (2022)
- [Melody transcription via generative pre-training](https://mlsanity.com/paper/2212.01884/) (2022)
- [Learning to Read and Follow Music in Complete Score Sheet Images](https://mlsanity.com/paper/2007.10736/) (2020)
- [Audio-Conditioned U-Net for Position Estimation in Full Sheet Images](https://mlsanity.com/paper/1910.07254/) (2019)
- [MIDI-Sheet Music Alignment Using Bootleg Score Synthesis](https://mlsanity.com/paper/2004.10345/) (2020)
- [Rethinking Evaluation Methodology for Audio-to-Score Alignment](https://mlsanity.com/paper/2009.14374/) (2020)
- [Audio-to-Score Alignment using Transposition-invariant Features](https://mlsanity.com/paper/1807.07278/) (2018)

## Built on by (most cited first, 2 of 2)

- [Real-world Music Plagiarism Detection With Music Segment Transcription System](https://mlsanity.com/paper/2509.08282/) (2025)
- [Local Multimodal Music Alignment from Global Supervision](https://mlsanity.com/paper/2607.10023/) (2026)

## Abstract (arXiv)

We propose an efficient workflow for high-quality offline alignment of in-the-wild performance audio and corresponding sheet music scans (images). Recent work on audio-to-score alignment extends dynamic time warping (DTW) to be theoretically able to handle jumps in sheet music induced by repeat signs-this method requires no human annotations, but we show that it often yields low-quality alignments. As an alternative, we propose a workflow and interface that allows users to quickly annotate jumps (by clicking on repeat signs), requiring a small amount of human supervision but yielding much higher quality alignments on average. Additionally, we refine audio and score feature representations to improve alignment quality by: (1) integrating measure detection into the score feature representation, and (2) using raw onset prediction probabilities from a music transcription model instead of piano roll. We propose an evaluation protocol for audio-to-score alignment that computes the distance between the estimated and ground truth alignment in units of measures. Under this evaluation, we find that our proposed jump annotation workflow and improved feature representations together improve alignment accuracy by 150% relative to prior work (33% to 82%).