VisionScores -- A system-segmented image score dataset for deep learning tasks
This provides a structured dataset for music-related machine learning tasks, though it is incremental as it focuses on a specific domain.
The authors introduced VisionScores, the first system-segmented image score dataset for deep learning, containing 24.8k grayscale images of two-handed piano pieces with metadata and full-page scores.
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 $128 \times 512$ pixels. VisionScores supplies the users not only the formatted samples but the systems' order and pieces' metadata. Moreover, unsegmented full-page scores and the pre-formatted images are included for further analysis.