Datasets:

charisreneec
/

OpenPIR

---
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](https://zenodo.org/record/1432913), a Creative Commons-licensed collection of 10-second music clips sourced from the [Free Music Archive](https://freemusicarchive.org/). 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](https://ieeexplore.ieee.org/document/11464738) · [Code & Demo](https://github.com/charisrenee/InstrumentRecognitionWithDiffusion)

---

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](https://www.upf.edu/web/mtg/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](#references) section below.

![SOTA comparison table](OpenPIR_PerformanceStat_SOTA.png)

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](OpenPIR_PerformanceStat_Han.png)

---

## 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

```python
from datasets import load_dataset
ds = load_dataset("charisreneec/OpenPIR", split="train")
print(ds[0])
```

### Map labels to IRMAS codes

```python
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:

```python
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](https://zenodo.org/record/1432913)
- Free Music Archive: [freemusicarchive.org](https://freemusicarchive.org/)

---

## Citation

If you use OpenPIR in your work, please cite:

```bibtex
@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:

```bibtex
@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

	---
	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](https://zenodo.org/record/1432913), a Creative Commons-licensed collection of 10-second music clips sourced from the [Free Music Archive](https://freemusicarchive.org/). 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](https://ieeexplore.ieee.org/document/11464738) · [Code & Demo](https://github.com/charisrenee/InstrumentRecognitionWithDiffusion)

	---

	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](https://www.upf.edu/web/mtg/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](#references) section below.

	![SOTA comparison table](OpenPIR_PerformanceStat_SOTA.png)

	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](OpenPIR_PerformanceStat_Han.png)

	---

	## 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

	```python
	from datasets import load_dataset

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

	### Map labels to IRMAS codes

	```python
	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:

	```python
	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](https://zenodo.org/record/1432913)
	- Free Music Archive: [freemusicarchive.org](https://freemusicarchive.org/)

	---

	## Citation

	If you use OpenPIR in your work, please cite:

	```bibtex
	@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:

	```bibtex
	@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