metadata

license: cc-by-nc-4.0
tags:
  - music
  - documents
  - end-to-end
  - full-page
  - system-level
annotations_creators:
  - manually expert-generated
pretty_name: Sheet Music Benchmark
size_categories:
  - 1K<n<10K
task_categories:
  - image-to-text
  - image-segmentation
  - text-retrieval
subtasks:
  - document-retrieval
extra_gated_fields:
  Affiliation: text

⚠️ Work in Progress! SMB: A Multi-Texture Sheet Music Recognition Benchmark ⚠️

Overview

SMB (Sheet Music Benchmark) is a dataset of printed Common Western Modern Notation scores developed at the University of Alicante at the Pattern Recognition and Artificial Intelligence Group.

Use Cases:

Optical Music Recognition (OMR): system-level, full-page
Image Segmentation: music regions

Dataset Details

Each page includes the corresponding **kern data for that specific page. Additionally, it provides detailed annotations for each region within the page.

1. Image

Type: PNG
Description: Encoded full-page image of the score.

2. Original Width

Type: Integer
Description: The width of the image in pixels.

3. Original Height

Type: Integer
Description: The height of the image in pixels.

4. Regions

Type: List of JSON objects
Description: Contains detailed information about regions on the page. Each JSON object includes:
- bbox:
  - x: The vertical position on the page (in pixels).
  - y: The horizontal position on the page (in pixels).
  - width: Width of the region (in pixels).
  - height: Height of the region (in pixels).
  - rotation: Angle of rotation (in degrees) for the bounding box around its top-left corner. This angle defines how much the box is rotated clockwise from its default unrotated position.
- raw: The content extracted from the original dataset before any processing.
- kern: A standardized version of the content ready for rendering.
- ekern: A tokenized and standardized version of the content for enhanced processing.

5. Page Texture

Type: String
Description: The musical texture of the page.
Values:
- "Pianoform"
- "Monophonic"
- "Other"

6. Page

Type: JSON object
Description: Metadata of the page. Fields include:
- raw: The unprocessed content extracted from the original dataset.
- kern: The content in a standardized format, ready to be rendered.
- ekern: The content in a tokenized and standardized format.

7. Score ID

Type: String
Description: Unique identifier for the original score to which the page belongs.

SMB usage 📖

SMB is publicly available at HuggingFace.

To download from HuggingFace:

Gain access to the dataset and get your HF access token from: https://huggingface.co/settings/tokens.
Install dependencies and login HF:
- Install Python
- Run pip install pillow datasets huggingface_hub[cli]
- Login by huggingface-cli login and paste the HF access token. Check here for details.
Use the following code to load SMB and extract the regions:

import math
from datasets import load_dataset
from PIL import ImageDraw


def draw_bounding_boxes(row):
  """
  Draws bounding boxes on an image based on region data provided in the row.

  Args:
      row (dict): A row from the dataset.
  Returns:
      PIL.Image: An image with bounding boxes drawn.
  """
  # Load the image
  image = row["image"]

  # Create a drawing context
  draw = ImageDraw.Draw(image)

  # Iterate through regions in the row
  for index, region in enumerate(row["regions"]):
      # Extract bounding box data
      bbox = region["bbox"]
      box_x = bbox["x"] / 100 * row["original_width"]
      box_y = bbox["y"] / 100 * row["original_height"]
      box_width = bbox["width"] / 100 * row["original_width"]
      box_height = bbox["height"] / 100 * row["original_height"]
      rotation = bbox["rotation"]

      # Convert rotation to radians
      rotation_rad = math.radians(rotation)

      # Calculate the corners relative to the top-left corner (anchor point)
      corners = [
          (0, 0),                     # Top-left
          (box_width, 0),             # Top-right
          (box_width, box_height),    # Bottom-right
          (0, box_height),            # Bottom-left
      ]

      # Apply rotation around the top-left corner
      rotated_corners = []
      for x, y in corners:
          rotated_x = box_x + x * math.cos(rotation_rad) - y * math.sin(rotation_rad)
          rotated_y = box_y + x * math.sin(rotation_rad) + y * math.cos(rotation_rad)
          rotated_corners.append((rotated_x, rotated_y))

      # Draw the rotated rectangle
      draw.polygon(rotated_corners, outline="red", width=3)

      # Show region data
      print(f"\nRegion {index}:"
            f"\nRotation (degrees): {rotation}"
            f"\nkern: {region['kern']}")

  return image


if __name__ == "__main__":
  # Load dataset from Hugging Face
  ds = load_dataset("PRAIG/SMB")

  # Select a subset of the dataset
  ds = ds["train"]

  # Iterate through rows in the dataset
  for row in ds:
      # Draw bounding boxes on the image
      image = draw_bounding_boxes(row)

      # Show the image and wait for user to close it
      image.show()
      input("Close the image window and press Enter to continue...")

Citation

If you use our work, please cite us:

@preprint{MartinezSevillaPRAIG24,
  author = {Juan C. Martinez{-}Sevilla and
            Noelia Luna{-}Barahona and
            Joan Cerveto{-}Serrano and
            Antonio Rios{-}Vila and
            David Rizo and
            Jorge Calvo{-}Zaragoza},
  title = {A Multi{-}Texture Sheet Music Recognition Benchmark},
  year = {2024}
}