# MIDI-Sheet Music Alignment Using Bootleg Score Synthesis

arXiv: https://arxiv.org/abs/2004.10345 · HTML: https://mlsanity.com/paper/2004.10345/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Structure-Aware Audio-to-Score Alignment using Progressively Dilated Convolutional Neural Networks](https://mlsanity.com/paper/2102.00382/) (2021)
- [A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization](https://mlsanity.com/paper/2204.08822/) (2022)
- [Just Label the Repeats for In-The-Wild Audio-to-Score Alignment](https://mlsanity.com/paper/2411.07428/) (2024)

## Abstract (arXiv)

MIDI-sheet music alignment is the task of finding correspondences between a MIDI representation of a piece and its corresponding sheet music images. Rather than using optical music recognition to bridge the gap between sheet music and MIDI, we explore an alternative approach: projecting the MIDI data into pixel space and performing alignment in the image domain. Our method converts the MIDI data into a crude representation of the score that only contains rectangular floating notehead blobs, a process we call bootleg score synthesis. Furthermore, we project sheet music images into the same bootleg space by applying a deep watershed notehead detector and filling in the bounding boxes around each detected notehead. Finally, we align the bootleg representations using a simple variant of dynamic time warping. On a dataset of 68 real scanned piano scores from IMSLP and corresponding MIDI performances, our method achieves a 97.3% accuracy at an error tolerance of one second, outperforming several baseline systems that employ optical music recognition.