OpenPIR: Open Predominant Instrument Recognition Dataset

OpenPIR is a hand-labeled dataset for predominant instrument recognition (PIR) built from OpenMic-2018, a Creative Commons-licensed collection of 10-second music clips sourced from the Free Music Archive. It was introduced as part of the ICASSP 2026 paper:

Leveraging Diffusion U-Net Features for Predominant Instrument Recognition
Charis Cochran, Yeongheon Lee, Youngmoo Kim — Drexel University / University of Pennsylvania
IEEE Xplore · Code & Demo

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Note: The OpenPIR predominant instrument and genre labels in this dataset are our original contribution. The remaining fields (artist, album, and additional tags) are not our work; they are sourced directly from OpenMic-2018 (Humphrey et al., 2018) and reproduced here solely to enable cross-referencing with the original OpenMic samples. Users should consult the original OpenMic-2018 release for the licensing and attribution terms governing that metadata.

Dataset Summary

IRMAS is the standard training dataset for predominant instrument recognition, but it has two well-known limitations: relatively small size (6,705 clips), and training examples with only one predomiant instrument label (even if more than one is present) that do not reflect the overlapping timbres of real-world music and the test set ( which may have 1-5 predominant labels per clip). OpenPIR addresses both by adding 1,228 annotated clips from OpenMic-2018 that are compatible with IRMAS labels — supplementing every instrument category and introducing genuine multi-label annotations.

Dataset Construction

OpenMic-2018 provides crowd-sourced instrument presence/absence scores (0–1) across 20 classes for ~20,000 clips from the Free Music Archive, but does not identify which instrument is predominant. To produce PIR-compatible labels, the following pipeline was applied:

1. Filter to IRMAS classes. OpenMic-2018 annotations were filtered to retain only the 11 instrument classes shared with IRMAS.
2. Candidate selection. From the filtered set, clips where exactly one instrument received a relevance score above 0.80 and all others fell below 0.40 were kept. This yielded ~4,500 candidates.
3. Manual labeling. Each candidate was listened to and annotated for:
   • Predominant instrument(s) — guitar was differentiated into acoustic vs. electric to match IRMAS classes
   • IRMAS-equivalent genre (mapped from FMA tags)
   • Drum/percussion presence (to match the IRMAS annotation convention)
4. Consistency check. Only clips where the labeled instrument was predominant for the entire 10-second clip were retained. For clips where the instrument was predominant for only part of the clip, a start/stop time was selected manually, keeping the segment above 3 seconds.
5. Genre exclusion. Clips from genres diverging strongly from IRMAS content (e.g., Drone, Glitch) were removed.

This process yielded 1,228 clips across 11 instrument classes.

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Dataset Statistics

	Count
Total clips	1,228
Single-instrument clips	905
Multi-instrument clips	323
Instrument classes	11 (+ "other instrument")
Genre classes	6 IRMAS genres + instrumental + soundtrack

Instrument Distribution

Labels are multi-hot (a clip can have more than one predominant instrument).

Instrument	IRMAS code	Label occurrences
Electric guitar	gel	222
Piano	pia	195
Voice / singing	voi	188
Cello	cel	172
Acoustic guitar	gac	166
Violin	vio	142
Organ	org	130
Saxophone	sax	107
Trumpet	tru	72
Other instrument	—	69
Flute	flu	68
Clarinet	cla	40

"Other instrument" is used for clips whose predominant instrument falls outside the 11 IRMAS classes (e.g., synthesizer, banjo, mandolin). These clips are included in the dataset but excluded from IRMAS-code evaluation.

Genre Distribution

Genre labels are mapped to the IRMAS genre taxonomy.

Genre	IRMAS code	Clips
Instrumental / no clear genre	instrumental	361
Pop / rock	pop_roc	328
Country / folk	cou_fol	300
Classical	cla	280
Jazz / blues	jaz_blu	168
Soundtrack / score	soundtrack	127
Latin / soul	lat_sou	65
Not available	NA	83

Genre counts exceed 1,228 because a small number of clips carry two genre labels. The NA category covers clips whose FMA genre tags could not be reliably mapped.

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Experimental Results

OpenPIR was used to fine-tune a baseline PIR model (trained on IRMAS + MUMS solo recordings) and measure the effect on held-out evaluation performance. Four model variants were compared:

Model	Training procedure
Baseline	Trained on IRMAS + MUMS solos, 400 epochs
Model A	Baseline fine-tuned 100 epochs on IRMAS only
Model B	Baseline fine-tuned 100 epochs on IRMAS + OpenPIR
Model C	Sequential fine-tune: B → 100 more epochs on IRMAS + OpenPIR

Comparison with prior work

Adding OpenPIR (≈3.2 hours of additional data) achieves competitive Micro and Macro F1 against systems that use substantially more augmentation data. Reference numbers correspond to the References section below.

¹ Yu et al. results reproduced from the original paper; evaluated on a different test split.

Per-class performance vs. Han et al. [2]

The chart below compares per-class Precision, Recall, and F1 for our best model (Model C) against a reimplementation of Han et al.

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Data Fields

The dataset is distributed as openpir_metadata.jsonl. Each line is a JSON object with the following fields:

Field	Type	Description
sample_key	string	OpenMic-2018 sample identifier ({track_id}_{start_frame})
filepath	string	Relative path to the audio file within the OpenMic-2018 archive
artist	string	Artist name from FMA metadata
title	string	Track title from FMA metadata
album	string	Album name from FMA metadata
instrument	list[string]	Predominant instrument(s), full English names (e.g. "acoustic guitar")
openmic_genres	list[string]	Original FMA genre tags carried through from OpenMic-2018
genre	list[string]	Genre(s) mapped to IRMAS taxonomy codes
tags	list[string]	Additional free-form tags (may be empty)

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How to Use

Load the metadata

from datasets import load_dataset

ds = load_dataset("charisreneec/OpenPIR", split="train")
print(ds[0])

Map labels to IRMAS codes

INSTRUMENT_TO_IRMAS = {
    "cello": "cel",
    "clarinet": "cla",
    "flute": "flu",
    "acoustic guitar": "gac",
    "electric guitar": "gel",
    "organ": "org",
    "piano": "pia",
    "saxophone": "sax",
    "trumpet": "tru",
    "violin": "vio",
    "voice": "voi",
}

def get_irmas_labels(example):
    return [INSTRUMENT_TO_IRMAS[i] for i in example["instrument"] if i in INSTRUMENT_TO_IRMAS]

Load audio (requires OpenMic-2018)

The audio files are not redistributed here because they originate from OpenMic-2018 (CC BY 4.0, Free Music Archive). Download the archive from Zenodo and point your loader at it:

import json, soundfile as sf
from pathlib import Path

openmic_root = Path("/path/to/openmic-2018")  # set to your local copy

with open("openpir_metadata.jsonl") as f:
    records = [json.loads(line) for line in f]

for record in records:
    audio_path = openmic_root / record["filepath"]
    audio, sr = sf.read(audio_path)
    labels = record["instrument"]
    # ... your processing here

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Source Data & License

OpenPIR labels are released under CC BY 4.0.

The underlying audio clips come from OpenMic-2018, which is itself derived from the Free Music Archive and also released under CC BY 4.0. If you use OpenPIR, you must also comply with the OpenMic-2018 license terms:

• OpenMic-2018: Humphrey et al., "OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition," ISMIR 2018. Zenodo record
• Free Music Archive: freemusicarchive.org

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Citation

If you use OpenPIR in your work, please cite:

@inproceedings{cochran2026openpir,
  title     = {Leveraging Diffusion U-Net Features for Predominant Instrument Recognition},
  author    = {Cochran, Charis and Lee, Yeongheon and Kim, Youngmoo},
  booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year      = {2026},
  url       = {https://ieeexplore.ieee.org/document/11464738}
}

and OpenMic-2018:

@inproceedings{humphrey2018openmic,
  title     = {{OpenMIC-2018}: An Open Data-set for Multiple Instrument Recognition},
  author    = {Humphrey, Eric J. and Durand, Simon and McFee, Brian},
  booktitle = {Proceedings of the International Society for Music Information Retrieval Conference (ISMIR)},
  year      = {2018}
}

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References

References cited in the SOTA comparison table, renumbered [1]–[8]:

[1] J. J. Bosch, J. Janer, F. Fuhrmann, and P. Herrera. "A Comparison of Sound Segregation Techniques for Predominant Instrument Recognition in Musical Audio Signals." ISMIR, 2012.

[2] Y. Han, J. Kim, and K. Lee. "Deep Convolutional Neural Networks for Predominant Instrument Recognition in Polyphonic Music." IEEE/ACM Trans. Audio, Speech, Lang. Process., 2017.

[3] J. Pons, O. Slizovskaia, R. Gong, E. Gómez, and X. Serra. "Timbre Analysis of Music Audio Signals with Convolutional Neural Networks." EUSIPCO, 2017.

[4] K. Avramidis, A. Kratimenos, C. Garoufis, and P. Maragos. "Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio." ICASSP, 2021.

[5] D. Yu, H. Wang, P. Chen, and Z. Wei. "Predominant Instrument Recognition Based on Deep Neural Network with Auxiliary Classification." IEEE/ACM Trans. Audio, Speech, Lang. Process. 28 (2020), pp. 852–861.

[6] A. Kratimenos, K. Avramidis, M. Kosta, and M. Kokiopoulou. "Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music." EUSIPCO, 2021.

[7] L. C. Reghunath and R. Rajan. "Transformer-Based Ensemble Method for Multiple Predominant Instruments Recognition in Polyphonic Music." EURASIP J. Audio, Speech, Music Process. 2022.1 (2022), p. 11.

[8] L. Zhong, Z. Chen, W. Liang, J. Li, and C. Shi. "Exploring Isolated Musical Notes as Pre-Training Data for Predominant Instrument Recognition in Polyphonic Music." APSIPA ASC, 2023, pp. 2312–2319.

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Contact

Charis Cochran — crc356 [at] drexel [dot] edu
Drexel University / University of Pennsylvania