Towards Linking the Lakh and IMSLP Datasets
Abstract
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.
Cite
@article{arxiv.2004.10391,
title = {Towards Linking the Lakh and IMSLP Datasets},
author = {TJ Tsai},
journal= {arXiv preprint arXiv:2004.10391},
year = {2020}
}
Comments
5 pages, 4 figures, 1 table. Accepted paper at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2020