# Learning Audio - Sheet Music Correspondences for Score Identification and Offline Alignment

arXiv: https://arxiv.org/abs/1707.09887 · HTML: https://mlsanity.com/paper/1707.09887/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization](https://mlsanity.com/paper/2204.08822/) (2022)
- [Artist Similarity with Graph Neural Networks](https://mlsanity.com/paper/2107.14541/) (2021)
- [Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems](https://mlsanity.com/paper/2309.12134/) (2023)
- [Proceedings of the 4th International Workshop on Reading Music Systems](https://mlsanity.com/paper/2211.13285/) (2022)
- [Passage Summarization with Recurrent Models for Audio-Sheet Music Retrieval](https://mlsanity.com/paper/2309.12111/) (2023)
- [Local Multimodal Music Alignment from Global Supervision](https://mlsanity.com/paper/2607.10023/) (2026)

## Abstract (arXiv)

This work addresses the problem of matching short excerpts of audio with their respective counterparts in sheet music images. We show how to employ neural network-based cross-modality embedding spaces for solving the following two sheet music-related tasks: retrieving the correct piece of sheet music from a database when given a music audio as a search query; and aligning an audio recording of a piece with the corresponding images of sheet music. We demonstrate the feasibility of this in experiments on classical piano music by five different composers (Bach, Haydn, Mozart, Beethoven and Chopin), and additionally provide a discussion on why we expect multi-modal neural networks to be a fruitful paradigm for dealing with sheet music and audio at the same time.