PRAIG/JAZZMUS

Optical Music Recognition of Jazz Lead Sheets

📄 Conf. materials | 🖥️ Slides | 🎶 Poster

Dataset

How to use

Check the following code:

import ast

from datasets import load_dataset
from PIL import ImageDraw


DATASET_NAME = "PRAIG/JAZZMUS"

ds = load_dataset(DATASET_NAME)

image = ds["train"][0]["image"]

# list of systems, with bounding boxes and encoding
systems = ast.literal_eval(ds["train"][0]["annotation"])["systems"]

# full page encodings
encoding = ast.literal_eval(ds["train"][0]["annotation"])["encodings"]
mxml_encoding = encoding["musicxml"]
kern_encoding = encoding["**kern"]

# draw the bounding boxes on the image
ImageDraw = ImageDraw.Draw(image)

for idx, s in enumerate(systems):
    print(f"System {idx + 1}:")
    print(f"\t{s['bounding_box']}")
    print(f"\t{repr(s['**kern'])}\n")
    bbox = s["bounding_box"]
    ImageDraw.rectangle(
        [bbox["fromX"], bbox["fromY"], bbox["toX"], bbox["toY"]],
        outline="red",
        width=2,
    )

image.save("image_with_bboxes.pdf", "PDF")

Other dataset versions

🤗 JAZZMUS dataset collection

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Citation

@inproceedings{juan_carlos_martinez_sevilla_2025_17811464,
  author       = {Juan Carlos Martinez-Sevilla and
                  Francesco Foscarin and
                  Patricia Garcia-Iasci and
                  David Rizo and
                  Jorge Calvo-Zaragoza and
                  Gerhard Widmer},
  title        = {Optical Music Recognition of Jazz Lead Sheets},
  booktitle    = {Proceedings of the 26th International Society for
                   Music Information Retrieval Conference
                  },
  year         = 2025,
  pages        = {710-716},
  publisher    = {ISMIR},
  month        = sep,
  venue        = {Daejeon, South Korea and Online},
  doi          = {10.5281/zenodo.17811464},
  url          = {https://doi.org/10.5281/zenodo.17811464},
}