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15th workshop gRFIA

9th Music Encoding Conference (MEI 2021)


Alicante, July 19-23

13th international workshop on Machine Learning and Music


(online) September 18, 2020



  1. Alfaro-Contreras, M.; Ríos-Vila, A.; Valero-Mas, J.J.; Iñesta, J.M.; Calvo-Zaragoza, J.
    "Decoupling music notation to improve end-to-end Optical Music Recognition"
    Pattern Recognition Letters, vol. 158 , pp. 157--163 (2022)
    : bibtex : DOI

    Inspired by the Text Recognition field, end-to-end schemes based on Convolutional Recurrent Neural Networks (CRNN) trained with the Connectionist Temporal Classification (CTC) loss function are considered one of the current state-of-the-art techniques for staff-level Optical Music Recognition (OMR). Unlike text symbols, music-notation elements may be defined as a combination of (i) a shape primitive located in (ii) a certain position in a staff. However, this double nature is generally neglected in the learning process, as each combination is treated as a single token. In this work, we study whether exploiting such particularity of music notation actually benefits the recognition performance and, if so, which approach is the most appropriate. For that, we thoroughly review existing specific approaches that explore this premise and propose different combinations of them. Furthermore, considering the limitations observed in such approaches, a novel decoding strategy specifically designed for OMR is proposed. The results obtained with four different corpora of historical manuscripts show the relevance of leveraging this double nature of music notation since it outperforms the standard approaches where it is ignored. In addition, the proposed decoding leads to significant reductions in the error rates with respect to the other cases.

@article {
 author = "Alfaro-Contreras, M.; Ríos-Vila, A.; Valero-Mas, J.J.; Iñesta, J.M.; Calvo-Zaragoza, J.",
 title  = "Decoupling music notation to improve end-to-end Optical Music Recognition",
 issn = "0167-8655",
 journal = "Pattern Recognition Letters",
 month = "June",
 pages = "157--163",
 volume = "158 ",
 year = "2022"
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