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---
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<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},
}
``` |