MIDI-Sheet Music Alignment Using Bootleg Score Synthesis
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.
- 2021Structure-Aware Audio-to-Score Alignment using Progressively Dilated Convolutional Neural Networkscited by 5
- 2022A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronizationcited by 5
- 2024Just Label the Repeats for In-The-Wild Audio-to-Score Alignmentcited by 2
- 2024Just Label the Repeats for In-The-Wild Audio-to-Score Alignment11 Nov 2024
- 2022A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization19 Apr 2022
- 2021Structure-Aware Audio-to-Score Alignment using Progressively Dilated Convolutional Neural Networks31 Jan 2021