@inproceedings{
gal{\'a}n-cuenca2024muscat,
title={{MUSCAT}: a Multimodal m{US}ic Collection for Automatic Transcription of real recordings and image scores},
author={Alejandro Gal{\'a}n-Cuenca and Jose J. Valero-Mas and Juan C. Martinez-Sevilla and Antonio Hidalgo-Centeno and Antonio Pertusa and Jorge Calvo-Zaragoza},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=B3CsOcxXOa}
}
Multimodal audio-image music transcription has been recently posed as a means of retrieving a digital score representation by leveraging the individual estimations from Automatic Music Transcription (AMT)---acoustic recordings---and Optical Music Recognition (OMR)---image scores---systems. Nevertheless, while proven to outperform single-modality recognition rates, this approach has been exclusively validated under controlled scenarios---monotimbral and monophonic synthetic data---mainly due to a lack of collections with symbolic score-level annotations for both recordings and graphical sheets. To promote research on this topic, this work presents the $\textit{Multimodal mUSic Collection for Automatic Transcription}$ (MUSCAT) assortment of acoustic recordings, image sheets, and their score-level annotations in several notation formats. This dataset comprises almost 80 hours of real recordings with varied instrumentation and polyphony degrees---from piano to orchestral music---1251 scanned sheets, and 880 symbolic scores from 37 composers, which may also be used in other tasks involving metadata such as instrument identification or composer recognition. A fragmented subset of this collection exclusively focused on acoustic data for score-level AMT---the $\textit{MUSic Collection for aUtomatic Transcription - fragmented Subset}$ (MUSCUTS) assortment---is also presented together with a baseline experimentation, concluding the need to foster research on this field with real recordings. Finally, a web-based service is also provided to increase the size of the collections collaboratively.
MUSCAT: a Multimodal mUSic Collection for Automatic Transcription of real recordings and image scores
Alejandro Galán-Cuenca Jose J. Valero-Mas Juan C. Martinez-Sevilla Antonio Hidalgo-Centeno Antonio Pertusa Jorge Calvo-Zaragoza