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Datasets:
MSRChallenge
/
2025TestSet

Tasks:
Audio-to-Audio
Modalities:
Audio
Text
Formats:
soundfolder
Languages:
No linguistic content
Size:
1K - 10K
Tags:
music
audio-restoration
source-separation
Libraries:
Datasets
License:
Dataset card Data Studio Files
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  1. README.md +93 -0
  2. metadata.jsonl +0 -0
  3. mixtures/Percussions/cyl_Percussions_004.flac +3 -0
  4. mixtures/Percussions/cyl_Percussions_005.flac +3 -0
  5. mixtures/Percussions/cyl_Percussions_008.flac +3 -0
  6. mixtures/Percussions/cyl_Percussions_009.flac +3 -0
  7. mixtures/Percussions/cyl_Percussions_012.flac +3 -0
  8. mixtures/Percussions/cyl_Percussions_013.flac +3 -0
  9. mixtures/Percussions/live_Percussions_000.flac +3 -0
  10. mixtures/Percussions/live_Percussions_001.flac +3 -0
  11. mixtures/Percussions/live_Percussions_011.flac +3 -0
  12. mixtures/Percussions/nb_Percussions_000.flac +3 -0
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  17. mixtures/Percussions/nb_Percussions_021.flac +3 -0
  18. mixtures/Percussions/nb_Percussions_036.flac +3 -0
  19. mixtures/Percussions/nb_Percussions_037.flac +3 -0
  20. mixtures/Percussions/nb_Percussions_040.flac +3 -0
  21. mixtures/Percussions/nb_Percussions_041.flac +3 -0
  22. mixtures/Percussions/nb_Percussions_056.flac +3 -0
  23. mixtures/Percussions/nb_Percussions_057.flac +3 -0
  24. mixtures/Percussions/nb_Percussions_060.flac +3 -0
  25. mixtures/Percussions/nb_Percussions_061.flac +3 -0
  26. mixtures/Percussions/nb_Percussions_076.flac +3 -0
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  28. mixtures/Percussions/nb_Percussions_082.flac +3 -0
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  30. mixtures/Percussions/nb_Percussions_094.flac +3 -0
  31. mixtures/Percussions/nb_Percussions_095.flac +3 -0
  32. mixtures/Percussions/nb_Percussions_098.flac +3 -0
  33. mixtures/Percussions/nb_Percussions_099.flac +3 -0
  34. mixtures/Percussions/nb_Percussions_104.flac +3 -0
  35. mixtures/Percussions/nb_Percussions_105.flac +3 -0
  36. mixtures/Percussions/nb_Percussions_108.flac +3 -0
  37. mixtures/Percussions/nb_Percussions_109.flac +3 -0
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  42. mixtures/Percussions/rad_Percussions_007.flac +3 -0
  43. mixtures/Percussions/rad_Percussions_010.flac +3 -0
  44. mixtures/Percussions/rad_Percussions_011.flac +3 -0
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  46. mixtures/Percussions/str_Percussions_003.flac +3 -0
  47. mixtures/Percussions/str_Percussions_005.flac +3 -0
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  49. mixtures/Percussions/str_Percussions_014.flac +3 -0
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README.md ADDED
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+ ---
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+ pretty_name: MSR Challenge 2025 Evaluation Set
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+ license: cc-by-nc-4.0
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+ task_categories:
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+ - audio-to-audio
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+ language:
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+ - zxx
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+ size_categories:
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+ - 1K<n<10K
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+ tags:
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+ - music
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+ - audio-restoration
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+ - source-separation
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+ ---
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+
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+ # MSR Challenge 2025 Evaluation Set
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+
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+ 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.
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+
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+ 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.
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+
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+ ## Dataset structure
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+
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+ ```text
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+ ./
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+ ├── metadata.jsonl
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+ ├── mixtures/
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+ │ └── {instrument}/{file_id}.flac
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+ └── targets/
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+ └── {instrument}/{file_id}.flac
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+ ```
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+
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+ All audio is stereo FLAC at 48 kHz and 10 seconds long. Mixtures and targets retain their original encoded bit depths.
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+
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+ | Subset | Mixtures | Targets | Description |
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+ |---|---:|---:|---|
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+ | `non-blind` | 1,000 | 1,000 | Organizer non-blind evaluation material |
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+ | `streaming` | 128 | 128 | Lossy-codec conditions |
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+ | `cylinder` | 112 | 0 | Historical cylinder recordings |
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+ | `live` | 128 | 0 | Live/acoustic degradation condition |
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+ | `radio` | 128 | 0 | Radio degradation condition |
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+ | **Total** | **1,496** | **1,128** | |
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+
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+ Instrument counts are 189 each for Bass, Drums, Guitars, Keyboards, Orchestral Elements, Percussions, and Vocals, and 173 for Synthesizers.
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+
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+ ## Metadata fields
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+
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+ - `file_id`: stable identifier used by the organizer archives.
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+ - `mixture_file_name`: relative path to the input audio.
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+ - `target_file_name`: relative path to the reference audio, or `null` when unavailable.
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+ - `has_target`: whether a reference is included.
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+ - `subset`: `non-blind`, `streaming`, `cylinder`, `live`, or `radio`.
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+ - `instrument`: target instrument class.
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+ - `augmentation_type`, `augmentation_code`: codec/augmentation description when present.
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+
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+ ## Intended use
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+
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+ 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.
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+
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+ ## Citation
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+
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+ Please cite the challenge summary when using this evaluation set. The original MSR task paper and the related MSRBench paper are also included below.
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+
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+ ```bibtex
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+ @inproceedings{zang2026msrchallenge,
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+ title = {Summary of the Inaugural Music Source Restoration Challenge},
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+ 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.},
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+ booktitle = {ICASSP 2026--2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
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+ pages = {21853--21855},
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+ year = {2026},
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+ doi = {10.1109/ICASSP55912.2026.11462762}
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+ }
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+
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+ @inproceedings{zang2025music,
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+ title = {Music Source Restoration},
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+ author = {Zang, Yongyi and Dai, Zheqi and Plumbley, Mark D. and Kong, Qiuqiang},
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+ booktitle = {2025 IEEE International Workshop on Multimedia Signal Processing (MMSP)},
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+ pages = {138--143},
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+ year = {2025},
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+ doi = {10.1109/MMSP64401.2025.11324269}
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+ }
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+
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+ @inproceedings{zang2026msrbench,
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+ title = {MSRBench: A Benchmarking Dataset for Music Source Restoration},
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+ 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.},
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+ booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
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+ year = {2026}
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+ }
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+ ```
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+
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+ ## License
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+
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+ This dataset is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/).
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