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OpenPIR

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metadata 
license: cc-by-4.0
task_categories:
  - audio-classification
tags:
  - audio
  - music
  - instrument-recognition
  - predominant-instrument
  - openmic
  - irmas
  - multi-label
pretty_name: OpenPIR
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: openpir_metadata.jsonl

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

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.

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.

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.

SOTA comparison table

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.

Per-class precision, recall, and F1 comparison

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)

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

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

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}
}

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.

Contact

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