• Retrieving Music Semantics from Optical Music Recognition by Machine Translation

    Author(s):
    Martha E. Thomae
    Contributor(s):
    Jorge Calvo-Zaragoza, José M. Iñesta, David Rizo, Antonio Ríos-Vila
    Editor(s):
    Elsa De Luca (see profile) , Julia Flanders
    Date:
    2020
    Group(s):
    Music Encoding Initiative
    Subject(s):
    Music, Digital humanities, Machine translating
    Item Type:
    Conference paper
    Conf. Title:
    Music Encoding Conference 2020
    Conf. Org.:
    Tisch Library, Tufts University
    Conf. Loc.:
    Online
    Conf. Date:
    26-29 May 2020
    Tag(s):
    music encoding, mir, deep learning, Machine translation
    Permanent URL:
    http://dx.doi.org/10.17613/605z-nt78
    Abstract:
    In this paper, we apply machine translation techniques to solve one of the central problems in the field of optical music recognition: extracting the semantics of a sequence of music characters. So far, this problem has been approached through heuristics and grammars, which are not generalizable solutions. We borrowed the seq2seq model and the attention mechanism from machine translation to address this issue. Given its example-based learning, the model proposed is meant to apply to different notations provided there is enough training data. The model was tested on the PrIMuS dataset of common Western music notation incipits. Its performance was satisfactory for the vast majority of examples, flawlessly extracting the musical meaning of 85% of the incipits in the test set—mapping correctly series of accidentals into key signatures, pairs of digits into time signatures, combinations of digits and rests into multi-measure rests, detecting implicit accidentals, etc.
    Notes:
    The MEC 2020 conference was originally to be hosted at Tisch Library and Lilly Music Library of Tufts University on the Medford, MA campus. It is co-sponsored with the Department of Music at Tufts, Digital Scholarship Group at Northeastern University Library, and MIT Digital Humanities.
    Metadata:
    xml
    Status:
    Published
    Last Updated:
    3 years ago
    License:
    Attribution-NonCommercial-NoDerivatives
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