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--- |
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language: en |
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tags: [music, multimodal, qa, midi, vision] |
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license: mit |
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--- |
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# MusiXQA 🎵 |
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**MusiXQA** is a multimodal dataset for evaluating and training music sheet understanding systems. Each data sample is composed of: |
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- A scanned music sheet image (`.png`) |
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- Its corresponding MIDI file (`.mid`) |
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- A structured annotation (from `metadata.json`) |
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- Question–Answer (QA) pairs targeting musical structure, semantics, and optical music recognition (OMR) |
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 |
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--- |
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## 📂 Dataset Structure |
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``` |
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MusiXQA/ |
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├── images.tar # PNG files of music sheets (e.g., 0000000.png) |
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├── midi.tar # MIDI files (e.g., 0000000.mid), compressed |
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├── train_qa_omr.json # OMR-tasks QA pairs (train split) |
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├── train_qa_simple.json # Simple musical info QAs (train split) |
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├── test_qa_omr.json # OMR-tasks QA pairs (test split) |
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├── test_qa_simple.json # Simple musical info QAs (test split) |
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├── metadata.json # Annotation for each document (e.g., key, time, instruments) |
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``` |
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## 🧾 Metadata |
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The `metadata.json` file provides comprehensive annotations of the full music sheet content, facilitating research in symbolic music reasoning, score reconstruction, and multimodal alignment with audio or MIDI. |
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## ❓ QA Data Format |
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Each QA file (e.g., train_qa_omr.json) is a list of QA entries like this: |
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``` |
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{ |
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"doc_id": "0086400", |
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"question": "Please extract the pitch and duration of all notes in the 2nd bar of the treble clef.", |
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"answer": "qB4~ sB4 e.E5~ sE5 sB4 eB4 e.E5 sG#4", |
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"encode_format": "beat" |
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}, |
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``` |
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• doc_id: corresponds to a sample in images/, midi/, and metadata.json<br /> |
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• question: natural language query<br /> |
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• answer: ground truth answer<br /> |
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• encode_format: how the input is encoded (e.g., "beat", "note", etc.)<br /> |
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## 🎓 Reference |
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If you use this dataset in your work, please cite it using the following reference: |
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``` |
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@misc{chen2025musixqaadvancingvisualmusic, |
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title={MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models}, |
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author={Jian Chen and Wenye Ma and Penghang Liu and Wei Wang and Tengwei Song and Ming Li and Chenguang Wang and Jiayu Qin and Ruiyi Zhang and Changyou Chen}, |
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year={2025}, |
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eprint={2506.23009}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2506.23009}, |
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} |
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``` |
