| | --- |
| | license: cc-by-4.0 |
| | size_categories: |
| | - 10K<n<100K |
| | tags: |
| | - music |
| | - art |
| | --- |
| | |
| | # VisionScores |
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| | **VisionScores** is a novel, system-segmented image score dataset specifically designed for machine learning applications in symbolic music processing. It represents the first dataset that captures the system-level structure of two-handed piano compositions, formatted for compatibility with modern machine learning frameworks. |
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| | The dataset was constructed to address the limitations of existing music score datasets, which are predominantly designed for Optical Music Recognition (OMR) tasks. **VisionScores** emphasizes structural consistency and format uniformity while preserving the semantic richness of musical scores. Its development was grounded on two foundational constraints: (1) content consistency, through the selection of two-handed piano compositions, and (2) format regularity, achieved through segmentation of systems from score pages into uniformly sized grayscale images. |
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| | The full methodology, motivation, and analysis are presented in the following paper: |
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| | **[VisionScores – A System-Segmented Image Score Dataset for Deep Learning Tasks](http://arxiv.org/abs/2506.23030)** |
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| | We invite readers to consult the full text for a comprehensive understanding of the dataset’s construction and applications. |
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| | ## Structure of Dataset |
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| | Each sample in **VisionScores** is a grayscale image of dimensions **128 × 512 pixels**, saved in `.jpg` format. All samples represent **individual systems** extracted from two-handed piano sheet music. In addition to the image data, **VisionScores** includes detailed **metadata** for each system, comprising the title of the piece, composer, key (if available), IMSLP page reference, and a system index. |
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| | To provide both structural similarity and stylistic diversity, the dataset is divided into two distinct scenarios: |
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| | * **Sonatinas Scenario**: Contains 14,000 systems extracted from various Sonatina compositions by multiple composers. This scenario emphasizes stylistic similarity across different authors. |
| | * **Franz Liszt Scenario**: Contains 10,810 systems extracted from diverse works by Franz Liszt, showcasing a wide range of compositional styles from a single composer. |
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| | These scenarios were designed to support controlled experimentation in symbolic music generation, layout analysis, and related tasks. |
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| | ### Disclaimer |
| | - As noted in the paper, **individual systems do not contain enough information to represent a work type or to characterize a composer**. These attributes emerge only in the context of complete, ordered sequences of systems. |
| | - The **system index** in the metadata is relative to the set of valid segmented systems for each piece and **may not** match the original order in the score. This limitation results from the exclusion of incomplete or low-quality segments and will be addressed in future dataset updates. |
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| | ## Additional Data |
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| | Auxiliary materials including unsegmented full-page scores and non-formatted systems are available through Google Drive due to file size constraints: |
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| | * [Google Drive – Auxiliary Files](https://drive.google.com/drive/folders/19ZJEfOZMDByBymQpXw3Y0Ys0Y4IwvZss?usp=drive_link) |
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| | Segmentation methods can be found on GitHub repository: |
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| | * [VisionScores GitHub](https://github.com/alroamz/VisionScores) |
