ai-forever/symurbench_datasets

SyMuRBench Datasets and Precomputed Features

This repository contains datasets and precomputed features for 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

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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

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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

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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.

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How to Use

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

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

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License

This dataset is released under the MIT License.

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Citation

If you use SyMuRBench in your work, please cite:

@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}
}