--- 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 **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