PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing
Paper • 2409.10831 • Published • 6
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value | family_id stringlengths 64 64 | split stringclasses 4
values | split_group stringlengths 24 24 | part_name stringlengths 0 88 | occurrences_json stringlengths 712 56.2k | matcher_flags_json stringclasses 1
value | start_tick int64 0 12.9B | end_tick int64 288 12.9B | ticks_per_beat int64 24 1.02k | note_count int64 6 32 | occurrence_count int64 2 100 | program int64 0 127 | duration_beats float64 4 32 | recurrence_score float64 0.66 1 | onsets_beats listlengths 6 32 | durations_beats listlengths 6 32 | pitches listlengths 6 32 | velocities listlengths 6 32 | midi_bytes unknown | midi_sha256 stringlengths 64 64 |
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End of preview. Expand in Data Studio
SaMuGeD Earworms recurring phrases
Main dataset of the SaMuGeD Earworms release (Ostinato / Catchy musical hooks). Source verified recurring MIDI phrases with separate drum patterns.
The release has three parts. This repository holds the phrases, splits, MIDI bytes, archives and the research note. The Space is the listening demo. The listening audio repository only stores the rendered loops the Space player streams and is not needed to use this dataset.
Dataset
| Release | Melodic | Percussion | Total |
|---|---|---|---|
| Lakh phrases (primary) | 50,566 | 44,511 | 95,077 |
PDMX expansion (pdmx_melodic) |
459,659 | 459,659 | |
MAESTRO expansion (maestro_melodic) |
3,423 | 3,423 |
| Source metadata | Lakh | MAESTRO | PDMX | Total |
|---|---|---|---|---|
Rows in source_terms, provenance_hints and work_identity |
17,232 | 1,276 | 189,704 | 208,212 |
The corpus expansion applies the same closed detector to PDMX and MAESTRO. pdmx_melodic holds 459,659 melodic phrases from 178,888 of the 189,704 PDMX scores and maestro_melodic holds 3,423 melodic phrases from 1,201 of the 1,276 MAESTRO performances. The remaining sources produced no phrase that passed the detector. Both use the phrase schema of the Lakh configurations. Artist and title come from the upstream score or performance metadata, not from file paths. Their split is unassigned, because no combined evaluation split has been made for the expanded corpora. Every PDMX batch and the MAESTRO run passed a full independent replay audit.
Rows contain note arrays, verified occurrence coordinates, source hashes and MIDI bytes. The processed Lakh MIDI Clean snapshot has 17,232 source paths, 16,995 successful parses and 237 recorded failures. The release passed complete artifact audits, with closed selection replay on a stratified 256 source sample.
from datasets import load_dataset
phrases = load_dataset("AlmazErmilov/samuged-recurring-phrases", "lakh_melodic", split="all")
with open("phrase.mid", "wb") as output:
output.write(phrases[0]["midi_bytes"])
lakh_melodic and lakh_percussion hold the Lakh phrases of the primary closed selection detector. Their Parquet files live in the data/closed_melodic and data/closed_percussion folders, named after the selection rule like the primary archive.
Filter the original split column before training. overlap_excluded and duplicate_excluded are not training or test rows. The primary archive includes manifests, provenance, split views, MIDI and checksums. Extract into an empty directory. Read the consumer guide.
The fixed window baseline that the research note compares against is kept for reproducibility only. It has 94,950 phrases, 50,439 melodic and 44,511 percussion, with selection replay on all successful sources. It stays available as the reference archive and as the Parquet folders data/reference_melodic and data/reference_percussion, but it is not a dataset configuration and the viewer does not show it.
Source metadata and rights evidence
Three configurations hold one row for each of the 208,212 sources of Lakh, MAESTRO and PDMX. They share the columns source_key, source_id, source_sha256 and dataset_id.
| Configuration | Content |
|---|---|
source_terms |
Corpus license, per score license declaration, MIDI copyright notices, declared research, sharing and commercial terms, usage conditions and their evidence |
provenance_hints |
Claim class, search route and hints read from existing labels, such as collection codes, dates and attribution markers |
work_identity |
MusicBrainz work candidates from the API lookup and three offline dump indexes (works by title, recordings by title and artist, classical works by composer and catalogue number), merged by work, with writers, ISWCs and an assessment tier |
These labels are evidence, not clearance. A corpus license or a per score declaration describes what the source states, not who owns the composition. A missing copyright notice does not mean the music is free of copyright. MusicBrainz candidates come from title and name agreement. They are unverified, they are never promoted to an accepted identity and some sources have several. Nothing in these configurations establishes composition, arrangement or performance rights. identity_status is candidate_unverified or unresolved and rights_clearance is always not_established. In work_identity, an API status of pending is historical. The dump indexes cover those sources instead.
Coverage of the identity layer differs by corpus. Lakh titles and artists are popular music that MusicBrainz lists well. PDMX is mostly traditional tunes transcribed from collections, which MusicBrainz rarely records as works. MAESTRO uses classical titles, which are matched through the composer and the catalogue number written in the title (opus, BWV, K., D., Hob. and similar systems). A Lakh artist written as a bare surname agrees with a single artist credit that contains it, recorded as the weaker surname_subset agreement. MusicBrainz lists many classical works more than once, so duplicate entries for one composer and catalogue number count as one work at the weaker tier, with the lowest work id as the representative.
| Corpus | Sources | At least one work candidate | Single candidate, full agreement | Main reason for the rest |
|---|---|---|---|---|
| Lakh | 17,232 | 83.8% | 39.9% | No MusicBrainz work with that title and artist |
| MAESTRO | 1,276 | 80.6% | 44.0% | No catalogue number in the title, or no work of the composer carries that number |
| PDMX | 189,704 | 0.5% | 0.2% | 75% are traditional collection transcriptions, 17.8% match by title only |
Every source has its corpus license and, for PDMX, its per score declaration in source_terms. The candidate percentages above say how many sources have a MusicBrainz work proposal, not how many are cleared.
Join phrases to their source metadata on source_id, which is unique across the three corpora. Byte identical files occur under several PDMX sources, so source_sha256 can match more than one metadata row.
from datasets import load_dataset
repo = "AlmazErmilov/samuged-recurring-phrases"
phrases = load_dataset(repo, "pdmx_melodic", split="all").remove_columns("midi_bytes").to_pandas()
terms = load_dataset(repo, "source_terms", split="all").to_pandas()
joined = phrases.merge(terms[["source_id", "source_sha256", "score_license_declaration", "overall_clearance_status"]],
on=["source_id", "source_sha256"], how="left", validate="many_to_one")
Listening and rankings
The Space is a curated listening demo, not the full dataset. Browse the dataset viewer above for more MIDI phrases. Additional web MIDI examples by Tool are demo supplements outside this dataset.
Three top 50 collections cover repeated motifs, popular songs and drums. The Space adds a personal Tool listening collection, including separate web MIDI supplements. These supplements are not included in the dataset. Filter by song or part, compare the layers and follow their source note attacks. A separate ten song selection uses published recognition or earworm occurrence evidence. Five CSV rankings are included. The original analytic popularity cohort uses an exact historical sales list match. The live Space applies a separate listening curation.
Playback loops until stopped. Download 48 kHz stereo PCM 24 bit WAV, FLAC or loop MIDI. FluidSynth with ColomboGMGS2 17.02 Vanilla renders the listening demo, preserving source notes, tempo and instrument programs. These are not commercial recording excerpts. The complete cycle can extend beyond the detector prototype. Selection and rendering details explain the evidence and source construction.
Limits and license
There are no listener labels for these fragments. Recurrence does not establish catchiness or recognition. Published familiarity evidence applies to songs only. Search limits, part selection, filename identities and duplicate arrangements affect coverage. This is not a perceptually validated benchmark. The experimental melody prior corpus is excluded.
The Lakh collection follows the upstream Lakh CC BY 4.0 declaration. Cite Colin Raffel's 2016 thesis, Learning Based Methods for Comparing Sequences, with Applications to Audio to MIDI Alignment and Matching. Composition and arrangement attribution remains incomplete. No independent rights clearance is claimed. Code is MIT. External evaluation datasets are excluded. SoundFont and interface font licenses are included in the Space. No DOI or peer review is claimed.
Licenses by configuration
| Configurations | License | Notes |
|---|---|---|
lakh_* and the reference archive |
CC BY 4.0 | Lakh MIDI declaration, composition attribution incomplete |
pdmx_melodic |
CC BY 4.0 | PDMX corpus license, with per score Public Domain Mark or CC0 declarations kept in source_terms |
maestro_melodic |
CC BY NC SA 4.0 | MAESTRO license, noncommercial use only and adapted material shared under the same license |
source_terms, provenance_hints and work_identity |
CC BY 4.0 | Fields taken from MusicBrainz are CC0 1.0 |
A Public Domain Mark is a declaration by the uploader, not a CC0 dedication and not a clearance of the underlying work. The MusicBrainz metadata license covers the metadata only, not the music it describes. Cite PDMX and MAESTRO when you use their configurations.
Authors
| Author | Contact |
|---|---|
| Peiyi Wu | pewu10205@uit.no |
| Asle Fjæran Øren | asleoren@gmail.com |
| Shayan Dadman | shayan.dadman@uit.no |
| Almaz Ermilov | almaz.ermilov@uit.no |
Citation
Copy the BibTeX below or download the bibliography. Citation metadata is also available. This is a dataset release, not a peer reviewed article.
@misc{wu2026samuged,
author = {Wu, Peiyi and Øren, Asle Fjæran and Dadman, Shayan and Ermilov, Almaz},
title = {{SaMuGeD} Earworms (Ostinato / Catchy musical hooks)},
year = {2026},
version = {0.3},
url = {https://huggingface.co/datasets/AlmazErmilov/samuged-recurring-phrases},
note = {Research dataset release}
}
Cite the upstream corpora for the expansion configurations.
- Phillip Long, Zachary Novack, Taylor Berg-Kirkpatrick and Julian McAuley (2024). PDMX, a large scale public domain MusicXML dataset for symbolic music processing. arXiv 2409.10831, accepted at ICASSP 2025. Dataset on Zenodo.
- Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel and Douglas Eck (2019). Enabling factorized piano music modeling and generation with the MAESTRO dataset. ICLR 2019, arXiv 1810.12247. Dataset on the Magenta site.
@inproceedings{long2025pdmx,
author = {Long, Phillip and Novack, Zachary and Berg-Kirkpatrick, Taylor and McAuley, Julian},
title = {{PDMX}: A Large-Scale Public Domain {MusicXML} Dataset for Symbolic Music Processing},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = {2025},
url = {https://arxiv.org/abs/2409.10831}
}
@inproceedings{hawthorne2019maestro,
author = {Hawthorne, Curtis and Stasyuk, Andriy and Roberts, Adam and Simon, Ian and Huang, Cheng-Zhi Anna and Dieleman, Sander and Elsen, Erich and Engel, Jesse and Eck, Douglas},
title = {Enabling Factorized Piano Music Modeling and Generation with the {MAESTRO} Dataset},
booktitle = {International Conference on Learning Representations},
year = {2019},
url = {https://arxiv.org/abs/1810.12247}
}
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