---
pretty_name: MSR Challenge 2025 Evaluation Set
license: cc-by-nc-4.0
task_categories:
- audio-to-audio
language:
- zxx
size_categories:
- 1K<n<10K
tags:
- music
- audio-restoration
- source-separation
---

# MSR Challenge 2025 Evaluation Set

This repository contains the held-out evaluation audio distributed for the inaugural Music Source Restoration (MSR) Challenge associated with ICASSP 2026. The task is to recover an unprocessed musical source from a professionally produced or otherwise degraded mixture.

The release has 1,496 stereo mixture clips across eight instrument classes. Reference targets are available for 1,128 examples; the remaining 368 examples are mixture-only because the organizer archive did not include references for the cylinder, live, or radio subsets.

## Dataset structure

```text
./
├── metadata.jsonl
├── mixtures/
│   └── {instrument}/{file_id}.flac
└── targets/
    └── {instrument}/{file_id}.flac
```

All audio is stereo FLAC at 48 kHz and 10 seconds long. Mixtures and targets retain their original encoded bit depths.

| Subset | Mixtures | Targets | Description |
|---|---:|---:|---|
| `non-blind` | 1,000 | 1,000 | Organizer non-blind evaluation material |
| `streaming` | 128 | 128 | Lossy-codec conditions |
| `cylinder` | 112 | 0 | Historical cylinder recordings |
| `live` | 128 | 0 | Live/acoustic degradation condition |
| `radio` | 128 | 0 | Radio degradation condition |
| **Total** | **1,496** | **1,128** | |

Instrument counts are 189 each for Bass, Drums, Guitars, Keyboards, Orchestral Elements, Percussions, and Vocals, and 173 for Synthesizers.

## Metadata fields

- `file_id`: stable identifier used by the organizer archives.
- `mixture_file_name`: relative path to the input audio.
- `target_file_name`: relative path to the reference audio, or `null` when unavailable.
- `has_target`: whether a reference is included.
- `subset`: `non-blind`, `streaming`, `cylinder`, `live`, or `radio`.
- `instrument`: target instrument class.
- `augmentation_type`, `augmentation_code`: codec/augmentation description when present.

## Intended use

This dataset is intended for evaluation of music source restoration and related source-separation or audio-restoration systems. Do not treat the mixture-only examples as having negative or silent targets. Users should report results separately by subset and instrument where possible.

## Citation

Please cite the challenge summary when using this evaluation set. The original MSR task paper and the related MSRBench paper are also included below.

```bibtex
@inproceedings{zang2026msrchallenge,
  title     = {Summary of the Inaugural Music Source Restoration Challenge},
  author    = {Zang, Yongyi and Hai, Jiarui and Ge, Wanying and Kong, Qiuqiang and Dai, Zheqi and Wang, Helin and Mitsufuji, Yuki and Plumbley, Mark D.},
  booktitle = {ICASSP 2026--2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages     = {21853--21855},
  year      = {2026},
  doi       = {10.1109/ICASSP55912.2026.11462762}
}

@inproceedings{zang2025music,
  title     = {Music Source Restoration},
  author    = {Zang, Yongyi and Dai, Zheqi and Plumbley, Mark D. and Kong, Qiuqiang},
  booktitle = {2025 IEEE International Workshop on Multimedia Signal Processing (MMSP)},
  pages     = {138--143},
  year      = {2025},
  doi       = {10.1109/MMSP64401.2025.11324269}
}

@inproceedings{zang2026msrbench,
  title     = {MSRBench: A Benchmarking Dataset for Music Source Restoration},
  author    = {Zang, Yongyi and Hai, Jiarui and Ge, Wanying and Dai, Zheqi and Wang, Helin and Mitsufuji, Yuki and Kong, Qiuqiang and Plumbley, Mark D.},
  booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year      = {2026}
}
```

## License

This dataset is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/).