# MIDI Passage Retrieval Using Cell Phone Pictures of Sheet Music

arXiv: https://arxiv.org/abs/2004.10347 · HTML: https://mlsanity.com/paper/2004.10347/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio](https://mlsanity.com/paper/2505.12863/) (2025)
- [Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems](https://mlsanity.com/paper/2309.12134/) (2023)
- [Passage Summarization with Recurrent Models for Audio-Sheet Music Retrieval](https://mlsanity.com/paper/2309.12111/) (2023)
- [PBSCR: The Piano Bootleg Score Composer Recognition Dataset](https://mlsanity.com/paper/2401.16803/) (2024)
- [Instrument Classification of Solo Sheet Music Images](https://mlsanity.com/paper/2609.18980/) (2026)

## Abstract (arXiv)

This paper investigates a cross-modal retrieval problem in which a user would like to retrieve a passage of music from a MIDI file by taking a cell phone picture of a physical page of sheet music. While audio-sheet music retrieval has been explored by a number of works, this scenario is novel in that the query is a cell phone picture rather than a digital scan. To solve this problem, we introduce a mid-level feature representation called a bootleg score which explicitly encodes the rules of Western musical notation. We convert both the MIDI and the sheet music into bootleg scores using deterministic rules of music and classical computer vision techniques for detecting simple geometric shapes. Once the MIDI and cell phone image have been converted into bootleg scores, we estimate the alignment using dynamic programming. The most notable characteristic of our system is that it does test-time adaptation and has no trainable weights at all -- only a set of about 30 hyperparameters. On a dataset containing 1000 cell phone pictures taken of 100 scores of classical piano music, our system achieves an F measure score of .869 and outperforms baseline systems based on commercial optical music recognition software.