Towards Linking the Lakh and IMSLP Datasets
arXiv:2004.10391 · doi:10.1109/ICASSP40776.2020.9053815
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.
5 pages, 4 figures, 1 table. Accepted paper at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2020
References in corpus (6)
- Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies
- Learning Audio - Sheet Music Correspondences for Score Identification and Offline Alignment
- Live Score Following on Sheet Music Images
- MIDI-Sheet Music Alignment Using Bootleg Score Synthesis
- Towards End-to-End Audio-Sheet-Music Retrieval
- MIDI Passage Retrieval Using Cell Phone Pictures of Sheet Music