• DocumentCode
    179710
  • Title

    Optical music recognition for traditional Thai sheet music

  • Author

    Kusakunniran, Worapan ; Prempanichnukul, Attapol ; Maneesutham, Arthid ; Chocksawud, Kullachut ; Tongsamui, Suparus ; Thongkanchorn, Kittikhun

  • Author_Institution
    Fac. of Inf. & Commun. Technol., Mahidol Univ., Bangkok, Thailand
  • fYear
    2014
  • fDate
    July 30 2014-Aug. 1 2014
  • Firstpage
    157
  • Lastpage
    162
  • Abstract
    Optical music recognition (OMR) is a system to transform a sheet music into a format readable by a machine. Over the last few years, several methods of OMR have been proposed for a standard sheet music. However, these methods cannot be applied directly for a traditional Thai sheet music (TSM) which uses Thai characters to represent music notes in the traditional way. This paper proposes a novel method to interpret an image of TSM into an editable or playable form. The proposed method contains three main processes including: 1) edge detection; 2) music note segmentation; and 3) music note recognition. First, canny edge detection is applied on an image of TSM. Second, music notes are individually segmented by using statistical analyses. Third, support vector machine (SVM) is used to recognize the segmented music notes. The experimental results based on many images of TSMs demonstrate that the proposed method can achieve very promising performance of above 80% accuracy, with a perfect performance (i.e. 100% accuracy) for some TSMs.
  • Keywords
    edge detection; image segmentation; music; optical character recognition; statistical analysis; support vector machines; Canny edge detection; OMR; SVM; TSM; Thai characters; Thai sheet music; edge detection; music note recognition; music note segmentation; optical music recognition; statistical analyses; support vector machine; Accuracy; Computer science; Image edge detection; Image segmentation; Indexes; Support vector machines; Training; Optical music recognition; Thai sheet music; canny edge detection; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Engineering Conference (ICSEC), 2014 International
  • Conference_Location
    Khon Kaen
  • Print_ISBN
    978-1-4799-4965-6
  • Type

    conf

  • DOI
    10.1109/ICSEC.2014.6978187
  • Filename
    6978187