Practical End-to-End Optical Music Recognition for Pianoform Music

• URL: http://arxiv.org/abs/2403.13763v1
• Date: Wed, 20 Mar 2024 17:26:22 GMT
• Title: Practical End-to-End Optical Music Recognition for Pianoform Music
• Authors: Jiří Mayer, Milan Straka, Jan Hajič jr., Pavel Pecina,
• Abstract summary: We define a sequential format called Linearized MusicXML, allowing to train an end-to-end model directly. We create a benchmarking typeset OMR with MusicXML ground truth based on the OpenScore Lieder corpus. 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.
• Score: 3.69298824193862
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The majority of recent progress in Optical Music Recognition (OMR) has been achieved with Deep Learning methods, especially models following the end-to-end paradigm, reading input images and producing a linear sequence of tokens. Unfortunately, many music scores, especially piano music, cannot be easily converted to a linear sequence. This has led OMR researchers to use custom linearized encodings, instead of broadly accepted structured formats for music notation. Their diversity makes it difficult to compare the performance of OMR systems directly. To bring recent OMR model progress closer to useful results: (a) We define a sequential format called Linearized MusicXML, allowing to train an end-to-end model directly and maintaining close cohesion and compatibility with the industry-standard MusicXML format. (b) We create a dev and test set for benchmarking typeset OMR with MusicXML ground truth based on the OpenScore Lieder corpus. They contain 1,438 and 1,493 pianoform systems, each with an image from IMSLP. (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. We also test our model against the recently published synthetic pianoform dataset GrandStaff and surpass the state-of-the-art results.

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