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Towards Full-Pipeline Handwritten OMR with Musical Symbol Detection by U-Nets

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10390147" target="_blank" >RIV/00216208:11320/18:10390147 - isvavai.cz</a>

  • Result on the web

    <a href="http://ismir2018.ircam.fr/doc/pdfs/175_Paper.pdf" target="_blank" >http://ismir2018.ircam.fr/doc/pdfs/175_Paper.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Towards Full-Pipeline Handwritten OMR with Musical Symbol Detection by U-Nets

  • Original language description

    Detecting music notation symbols is the most immediate unsolved subproblem in Optical Music Recognition for musical manuscripts. We show that a U-Net architecture for semantic segmentation combined with a trivial detector already establishes a high baseline for this task, and we propose tricks that further improve detection performance: training against convex hulls of symbol masks, and multichannel output models that enable feature sharing for semantically related symbols. The latter is helpful especially for clefs, which have severe impacts on the overall OMR result. We then integrate the networks into an OMR pipeline by applying a subsequent notation assembly stage, establishing a new baseline result for pitch inference in handwritten music at an f-score of 0.81. Given the automatically inferred pitches we run retrieval experiments on handwritten scores, providing first empirical evidence that utilizing the powerful image processing models brings content-based search in large musical manuscript arc

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 19th Conference of the International Society for Music Information Retrieval

  • ISBN

    978-2-9540351-2-3

  • ISSN

  • e-ISSN

    neuvedeno

  • Number of pages

    8

  • Pages from-to

    225-232

  • Publisher name

    International Society for Music Information Retrieval

  • Place of publication

    New York, NY, USA

  • Event location

    Paris, France

  • Event date

    Sep 24, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article