Year: 2025
Type: conference-paper
Abstract: VisionScores presents a novel proposal being the first system-segmented image score dataset, aiming to offer structure-rich, high information-density images for machine and deep learning tasks. Delimited to two-handed piano pieces, it was built to consider not only certain graphic similarity but also composition patterns, as this creative process is highly instrument-dependent. It provides two scenarios in relation to composer and composition type. The first, formed by 14k samples, considers works from different authors but the same composition type, specifically, Sonatinas. The latter, consisting of 10.8K samples, presents the opposite case, various composition types from the same author, being the one selected Franz Liszt. All of the 24.8k samples are formatted as grayscale jpg images of 128 × 512 pixels. VisionScores supplies the users not only the formatted samples but the systems’ order and pieces’ meta-data. Moreover, unsegmented full-page scores and the pre-formatted images are included for further analysis.VisionScores is freely available in https://github.com/alroamz/VisionScores.
Institution: Mathematics Research Center
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413278123