---
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)
![demo1](https://puar-playground.github.io/assets/img/2025-06-27/demo.jpg)

---

## 📂 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.
![demo2](https://puar-playground.github.io/assets/img/2025-06-27/header.jpg)


## ❓ 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<br />
•	question: natural language query<br />
•	answer: ground truth answer<br />
•	encode_format: how the input is encoded (e.g., "beat", "note", etc.)<br />

## 🎓 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}, 
}
```