MIDI-Sheet Music Alignment Using Bootleg Score Synthesis
This addresses alignment for music processing applications, but it is incremental as it builds on existing alignment methods with a novel projection approach.
The paper tackled MIDI-sheet music alignment by projecting MIDI data into pixel space and aligning crude score representations, achieving 97.3% accuracy at a one-second error tolerance on a dataset of 68 real scanned piano scores.
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.