---
license: apache-2.0
task_categories:
  - question-answering
  - visual-question-answering
language:
  - en
tags:
  - music
  - music-understanding
  - sheet-music
  - abc-notation
  - benchmark
  - multimodal
pretty_name: "MSU-Bench: Musical Score Understanding Benchmark"
size_categories:
  - 1K<n<10K
configs:
  - config_name: nested
    data_files:
      - split: test
        path: nested/test-*
  - config_name: flat
    data_files:
      - split: test
        path: flat/test-*
    default: true
dataset_info:
  - config_name: nested
    features:
      - name: song_id
        dtype: string
      - name: abc_notation
        dtype: string
      - name: pdf
        dtype: binary
      - name: images
        sequence: image
      - name: questions
        struct:
          - name: level
            sequence: int32
          - name: question
            sequence: string
          - name: answer
            sequence: string
    splits:
      - name: test
        num_examples: 150
  - config_name: flat
    features:
      - name: song_id
        dtype: string
      - name: abc_notation
        dtype: string
      - name: level
        dtype: int32
      - name: question
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: test
        num_examples: 1800
---

# MSU-Bench: Musical Score Understanding Benchmark

[![arXiv](https://img.shields.io/badge/arXiv-2511.20697-b31b1b.svg)](https://arxiv.org/abs/2511.20697)
[![HuggingFace](https://img.shields.io/badge/HuggingFace-Dataset-yellow.svg)](https://huggingface.co/datasets/Krinos/MSU-Bench)

**Evaluating Large Language Models' Comprehension of Complete Musical Scores**

---

## Overview

MSU-Bench is a human-curated benchmark for evaluating the musical score understanding capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs). It supports multimodal evaluation through both textual (ABC notation) and visual (PDF/image) inputs.

**Key Statistics:**
- 150 complete musical scores
- 1,800 generative question-answer pairs
- 4 hierarchical difficulty levels
- 12 questions per score (3 per level)

## Multi-level Understanding

![ABC notation example](figures/abc.png)

## Data Curation and Evaluation

![Data curation pipeline](figures/data.png)

## Difficulty Levels

| Level | Focus | Examples |
|-------|-------|----------|
| **Level 1** - Onset Information | Metadata at the beginning of a score | Composer, title, key, time signature, tempo, instrumentation, anacrusis |
| **Level 2** - Notation & Note | Local bar-level notation details | Pitch range, accidentals, dynamics, articulations, ornaments, tempo changes |
| **Level 3** - Chord & Harmony | Harmonic structures and progressions | Chord qualities, inversions, cadences, modulations, pedal points |
| **Level 4** - Texture & Form | Large-scale structural analysis | Melodic motifs, thematic organisation, texture types, formal design |

## Dataset Configs

The dataset is available in two configurations:

### `flat` (default) — 1 question per row, 1,800 rows

Each row contains a single question-answer pair with the score's ABC notation. Best for evaluation pipelines.

| Column | Type | Description |
|--------|------|-------------|
| `song_id` | `string` | Unique identifier derived from the score filename |
| `abc_notation` | `string` | Full ABC notation (text-based symbolic representation) |
| `level` | `int32` | Difficulty level (1–4) |
| `question` | `string` | The question text |
| `answer` | `string` | The reference answer |

### `nested` — 12 questions per score, 150 rows

Each row contains one complete score with all 12 questions nested, plus the PDF and page images. Best for per-score or visual analysis.

| Column | Type | Description |
|--------|------|-------------|
| `song_id` | `string` | Unique identifier |
| `abc_notation` | `string` | Full ABC notation |
| `pdf` | `binary` | The original rendered PDF |
| `images` | `list[image]` | Individual page images (PNG), 1–35 pages per score |
| `questions` | `struct{level, question, answer}` | 12 questions (3 per difficulty level) with reference answers |

> **Note:** PDF and page images are only stored in the `nested` config to avoid duplication. Use `song_id` to join with `flat` if needed.

### Modalities for Evaluation

| Modality | Input | Target Models |
|----------|-------|---------------|
| Textual QA | ABC notation + question | LLMs |
| Visual QA | PDF/images + question | VLMs |

## Repertoire

The benchmark covers 150 scores from the Western art music canon, spanning:
- **Periods:** Baroque, Classical, Romantic, Impressionism, 20th Century
- **Composers:** Bach, Beethoven, Chopin, Brahms, Debussy, Liszt, Schubert, Mozart, Mussorgsky, Grieg, and others
- **Genres:** Sonatas, character pieces, fugues, waltzes, nocturnes, etudes, rhapsodies, symphonies, concertos, art songs

## Usage

### Loading the Flat Config (default)

```python
from datasets import load_dataset

ds = load_dataset("Krinos/MSU-Bench", split="test")
print(len(ds))  # 1800

sample = ds[0]
print(sample["song_id"])
print(f"Level {sample['level']}: {sample['question']} -> {sample['answer']}")
```

### Loading the Nested Config

```python
ds_nested = load_dataset("Krinos/MSU-Bench", "nested", split="test")
print(len(ds_nested))  # 150

sample = ds_nested[0]
for lvl, q, a in zip(
    sample["questions"]["level"],
    sample["questions"]["question"],
    sample["questions"]["answer"],
):
    print(f"  L{lvl}: {q} -> {a}")
```

### Visual QA with Page Images

```python
ds_nested = load_dataset("Krinos/MSU-Bench", "nested", split="test")
sample = ds_nested[0]
for i, img in enumerate(sample["images"]):
    img.save(f"page_{i}.png")
```

### Joining Flat + Nested for Visual Evaluation

```python
ds_flat = load_dataset("Krinos/MSU-Bench", split="test")
ds_nested = load_dataset("Krinos/MSU-Bench", "nested", split="test")

# Build a lookup from song_id to images
images_lookup = {row["song_id"]: row["images"] for row in ds_nested}

# Get images for a flat row
sample = ds_flat[0]
images = images_lookup[sample["song_id"]]
```

## Citation

```bibtex
@article{dai2025msubench,
  title={Musical Score Understanding Benchmark: Evaluating Large Language Models' Comprehension of Complete Musical Scores},
  author={Dai, Congren and Yang, Yue and Li, Krinos and Zhou, Huichi and Liang, Shijie and Zhang, Bo and Liu, Enyang and Jin, Ge and An, Hongran and Zhang, Haosen and Jing, Peiyuan and Lee, Kinhei and Zhang, Zhenxuan and Li, Xiaobing and Sun, Maosong},
  journal={arXiv preprint arXiv:2511.20697},
  year={2025}
}
```

## License

Apache 2.0. Please also refer to the repository for licensing information regarding the musical scores sourced from MuseScore.

## Acknowledgements

This work is supported by the Advanced Discipline Construction Project of Beijing Universities, the Special Programme of National Natural Science Foundation of China (Grant No. T2341003), and the Major Programme of National Social Science Fund of China (Grant No. 21ZD19).