README.md

metadata

language: en
tags:
  - music
  - multimodal
  - qa
  - midi
  - vision
license: mit

MusiXQA 🎵

MusiXQA is a multimodal dataset for evaluating and training music sheet understanding systems. Each data sample is composed of:

• A scanned music sheet image (.png)
• Its corresponding MIDI file (.mid)
• A structured annotation (from metadata.json)
• Question–Answer (QA) pairs targeting musical structure, semantics, and optical music recognition (OMR)

---

📂 Dataset Structure

MusiXQA/
├── images.tar             # PNG files of music sheets (e.g., 0000000.png)
├── midi.tar               # MIDI files (e.g., 0000000.mid), compressed
├── train_qa_omr.json      # OMR-tasks QA pairs (train split)
├── train_qa_simple.json   # Simple musical info QAs (train split)
├── test_qa_omr.json       # OMR-tasks QA pairs (test split)
├── test_qa_simple.json    # Simple musical info QAs (test split)
├── metadata.json          # Annotation for each document (e.g., key, time, instruments)

🧾 Metadata

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.

❓ QA Data Format

Each QA file (e.g., train_qa_omr.json) is a list of QA entries like this:

{
    "doc_id": "0086400",
    "question": "Please extract the pitch and duration of all notes in the 2nd bar of the treble clef.",
    "answer": "qB4~ sB4 e.E5~ sE5 sB4 eB4 e.E5 sG#4",
    "encode_format": "beat"
  },

• doc_id: corresponds to a sample in images/, midi/, and metadata.json
• question: natural language query
• answer: ground truth answer
• encode_format: how the input is encoded (e.g., "beat", "note", etc.)

🎓 Reference

If you use this dataset in your work, please cite it using the following reference:

@misc{chen2025musixqaadvancingvisualmusic,
      title={MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models}, 
      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},
      year={2025},
      eprint={2506.23009},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.23009}, 
}