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A Deep Approach for Handwritten Musical Symbols Recognition

Authors:

Roberto M. Pinheiro Pereira,

Caio E.F. Matos,

Geraldo Braz Junior,

João D.S. de Almeida,

Anselmo C. de PaivaAuthors Info & Claims

Webmedia '16: Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web

Pages 191 - 194

https://doi.org/10.1145/2976796.2988171

Published: 08 November 2016 Publication History

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Abstract

Preserving the world musical heritage comes down to digitalising and provision of music works to further query on the acquired data. However, to do the processing it is necessary an Optical Music Recognition (OMR) system capable of decoding the original manuscripts into a machine-readable data. Developing a precise and robust OMR system for handwritten musical scores is still an open issue. A fundamental step of improve such task is to recognise musical notes. Hence, trying to provide ways to produce a truly robust OMR sys- tem, we present in this paper a new methodology applying deep learning techniques to recognise musical notes in digitalised handwritten musical scores. The proposed methodology has been tested on a ground truth dataset of music scores reaching a minimum error rate of 3.99%, 96.46% of precision and 96.56% of recall on the HOMUS dataset.

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Cited By

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Yu PChen H(2024)Deep Multilevel Cascade Residual Recurrent Framework (MCRR) for Sheet Music RecognitionIEEE Access10.1109/ACCESS.2024.335088012(6941-6960)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3350880
Malakar SGhosh MChaterjee ABhowmik SSarkar R(2020)Offline music symbol recognition using Daisy feature and quantum Grey wolf optimization based feature selectionMultimedia Tools and Applications10.1007/s11042-020-09638-3Online publication date: 25-Aug-2020
https://doi.org/10.1007/s11042-020-09638-3
Mengarelli LKostiuk BVitório JTibola MWolff WSilla C(2019)OMR metrics and evaluation: a systematic reviewMultimedia Tools and Applications10.1007/s11042-019-08200-0Online publication date: 14-Dec-2019
https://doi.org/10.1007/s11042-019-08200-0
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Index Terms

A Deep Approach for Handwritten Musical Symbols Recognition
1. Human-centered computing
  1. Accessibility
  2. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Hypertext / hypermedia

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Information

Published In

Webmedia '16: Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web

November 2016

384 pages

ISBN:9781450345125

DOI:10.1145/2976796

General Chairs:
Fábio de Jesus Lima Gomes
IFPI, Brazil
,
Ricardo de Andrade Lira Rabelo
UFPI, Brazil
,
Carlos de Salles Soares Neto
UFMA, Brazil
,
Program Chairs:
Roberto Willrich
UFSC, Brazil
,
Cesar Augusto Camillo Teixeira
UFSCar, Brazil
,
Jussara Marques de Almeida
UFMG, Brazil
,
Windson Viana de Carvalho
UFC, Brazil

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 November 2016

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Webmedia '16: 22nd Brazilian Symposium on Multimedia and the Web

November 8 - 11, 2016

Piauí State, Teresina, Brazil

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Webmedia '16 Paper Acceptance Rate 29 of 94 submissions, 31%;

Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

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Yu PChen H(2024)Deep Multilevel Cascade Residual Recurrent Framework (MCRR) for Sheet Music RecognitionIEEE Access10.1109/ACCESS.2024.335088012(6941-6960)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3350880
Malakar SGhosh MChaterjee ABhowmik SSarkar R(2020)Offline music symbol recognition using Daisy feature and quantum Grey wolf optimization based feature selectionMultimedia Tools and Applications10.1007/s11042-020-09638-3Online publication date: 25-Aug-2020
https://doi.org/10.1007/s11042-020-09638-3
Mengarelli LKostiuk BVitório JTibola MWolff WSilla C(2019)OMR metrics and evaluation: a systematic reviewMultimedia Tools and Applications10.1007/s11042-019-08200-0Online publication date: 14-Dec-2019
https://doi.org/10.1007/s11042-019-08200-0
Calvo-Zaragoza JRizo D(2018)End-to-End Neural Optical Music Recognition of Monophonic ScoresApplied Sciences10.3390/app80406068:4(606)Online publication date: 11-Apr-2018
https://doi.org/10.3390/app8040606
Pacha AEidenberger H(2017)Towards Self-Learning Optical Music Recognition2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2017.00-60(795-800)Online publication date: Dec-2017
https://doi.org/10.1109/ICMLA.2017.00-60
Calvo-Zaragoza JGallego APertusa A(2017)Recognition of Handwritten Music Symbols with Convolutional Neural Codes2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2017.118(691-696)Online publication date: Nov-2017
https://doi.org/10.1109/ICDAR.2017.118

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UrduDeepNet: offline handwritten Urdu character recognition using deep neural network

RATNet: A deep learning model for Bengali handwritten characters recognition

Online Recognition System for Handwritten Arabic Chemical Symbols

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