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Found 6 papers, 2 papers with code
Practical End-to-End Optical Music Recognition for Pianoform Music
(c) We train and fine-tune an end-to-end model to serve as a baseline on the dataset and employ the TEDn metric to evaluate the model.
Proceedings of the 1st International Workshop on Reading Music Systems
The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists.
Understanding Optical Music Recognition
For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR).
Attention as a Perspective for Learning Tempo-invariant Audio Queries
Current models for audio--sheet music retrieval via multimodal embedding space learning use convolutional neural networks with a fixed-size window for the input audio.
Detecting Noteheads in Handwritten Scores with ConvNets and Bounding Box Regression
Noteheads are the interface between the written score and music.
In Search of a Dataset for Handwritten Optical Music Recognition: Introducing MUSCIMA++
Optical Music Recognition (OMR) has long been without an adequate dataset and ground truth for evaluating OMR systems, which has been a major problem for establishing a state of the art in the field.
