---
license: mit
tags:
  - symbolic-music
  - music-information-retrieval
  - classification
  - retrieval
  - benchmark
---

# SyMuRBench Datasets and Precomputed Features

This repository contains datasets and precomputed features for [SyMuRBench](https://github.com/Mintas/SyMuRBench), a benchmark for symbolic music understanding models. It includes metadata and MIDI files for multiple classification and retrieval tasks, along with pre-extracted **music21** and **jSymbolic** features.

You can install and use the full pipeline via:
👉 [https://github.com/Mintas/SyMuRBench](https://github.com/Mintas/SyMuRBench)

---

## Overview

SyMuRBench supports evaluation across diverse symbolic music tasks, including composer, genre, emotion, and instrument classification, as well as score-performance retrieval. This Hugging Face dataset provides:

- Dataset metadata (CSV files)
- MIDI files organized by task
- Precomputed **music21** and **jSymbolic** features
- Configuration-ready structure for immediate use in benchmarking

---

## Tasks Description

| Task Name | Source Dataset | Task Type | # of Classes | # of Files | Default Metrics |
|----------|----------------|-----------|--------------|------------|-----------------|
| ComposerClassificationASAP | ASAP | Multiclass Classification | 7 | 197 | weighted f1 score, balanced accuracy |
| GenreClassificationMMD | MetaMIDI | Multiclass Classification | 7 | 2,795 | weighted f1 score, balanced accuracy |
| GenreClassificationWMTX | WikiMT-X | Multiclass Classification | 8 | 985 | weighted f1 score, balanced accuracy |
| EmotionClassificationEMOPIA | Emopia | Multiclass Classification | 4 | 191 | weighted f1 score, balanced accuracy |
| EmotionClassificationMIREX | MIREX | Multiclass Classification | 5 | 163 | weighted f1 score, balanced accuracy |
| InstrumentDetectionMMD | MetaMIDI | Multilabel Classification | 128 | 4,675 | weighted f1 score |
| ScorePerformanceRetrievalASAP | ASAP | Retrieval | - | 438 (219 pairs) | R@1, R@5, R@10, Median Rank |

---

## Precomputed Features

Precomputed features are available in the `data/features/` folder:
- `music21_full_dataset.parquet`
- `jsymbolic_full_dataset.parquet`

Each file contains a unified table with:
- `midi_file`: Filename of the MIDI
- `task`: Corresponding task name
- `E_0` to `E_N`: Feature vector

### Example

| midi_file | task | E_0 | E_1 | ... | E_672 | E_673 |
|----------|------|-----|-----|-----|-------|-------|
| Q1_0vLPYiPN7qY_1.mid | EmotionClassificationEMOPIA | 0.0 | 0.0 | ... | 0.0 | 0.0 |
| Q1_4dXC1cC7crw_0.mid | EmotionClassificationEMOPIA | 0.0 | 0.0 | ... | 0.0 | 0.0 |

## File Structure

The dataset is distributed as a ZIP archive:

`data/datasets.zip`

After extraction, the structure is:

```
datasets/
├── composer_and_retrieval_datasets/
│   ├── metadata_composer_dataset.csv
│   ├── metadata_retrieval_dataset.csv
│   └── ... (MIDI files organized in subfolders)
├── genre_dataset/
│   ├── metadata_genre_dataset.csv
│   └── midis/
├── wikimtx_dataset/
│   ├── metadata_wikimtx_dataset.csv
│   └── midis/
├── emopia_dataset/
│   ├── metadata_emopia_dataset.csv
│   └── midis/
├── mirex_dataset/
│   ├── metadata_mirex_dataset.csv
│   └── midis/
└── instrument_dataset/
    ├── metadata_instrument_dataset.csv
    └── midis/
```
* CSV files: Contain `filename` and `label` (or pair info for retrieval).
* MIDI files: Used as input for feature extractors.

---

## How to Use

You can download and extract everything using the built-in utility:

```python
from symurbench.utils import load_datasets

load_datasets(output_folder="./data", load_features=True)
```

This will:

* Download datasets.zip and extract it
* Optionally download precomputed features
* Update config paths automatically

---

## License

This dataset is released under the MIT License.

---

## Citation

If you use SyMuRBench in your work, please cite:

```bibtex
@inproceedings{symurbench2025,
  author    = {Petr Strepetov and Dmitrii Kovalev},
  title     = {SyMuRBench: Benchmark for Symbolic Music Representations},
  booktitle = {Proceedings of the 3rd International Workshop on Multimedia Content Generation and Evaluation: New Methods and Practice (McGE '25)},
  year      = {2025},
  pages     = {9},
  publisher = {ACM},
  address   = {Dublin, Ireland},
  doi       = {10.1145/3746278.3759392}
}
```