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README.md

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----
-license: apache-2.0
----
+---
+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
+---
+
+# 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)
+
+---
+
+## 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 studio-recorded solo performances that do not reflect the overlapping timbres of real-world music. 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.
+
+![SOTA comparison table](OpenPIR_PerformanceStat_SOTA.png)
+
+### Per-class performance vs. Han et al. baseline
+
+The chart below compares per-class Precision, Recall, and F1 for our best model 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}
+}
+```
+
+---
+
+## Contact
+
+Charis Cochran — [charisreneec@gmail.com](mailto:charisreneec@gmail.com)  
+Drexel University / University of Pennsylvania