# Towards Linking the Lakh and IMSLP Datasets

arXiv: https://arxiv.org/abs/2004.10391 · HTML: https://mlsanity.com/paper/2004.10391/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

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

- [Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems](https://mlsanity.com/paper/2309.12134/) (2023)
- [Towards Robust and Truly Large-Scale Audio-Sheet Music Retrieval](https://mlsanity.com/paper/2309.12158/) (2023)
- [Passage Summarization with Recurrent Models for Audio-Sheet Music Retrieval](https://mlsanity.com/paper/2309.12111/) (2023)

## Abstract (arXiv)

This paper investigates the problem of matching a MIDI file against a large database of piano sheet music images. Previous sheet-audio and sheet-MIDI alignment approaches have primarily focused on a 1-to-1 alignment task, which is not a scalable solution for retrieval from large databases. We propose a method for scalable cross-modal retrieval that might be used to link the Lakh MIDI dataset with IMSLP sheet music data. Our approach is to modify a previously proposed feature representation called a symbolic bootleg score to be suitable for hashing. On a database of 5,000 piano scores containing 55,000 individual sheet music images, our system achieves a mean reciprocal rank of 0.84 and an average retrieval time of 25.4 seconds.