2013
Calvo-Zaragoza, J.; Oncina, J.
Human-Computer Interaction for Optical Music Recognition tasks Proceedings Article
In: Actas del III Workshop de Reconocimiento de Formas y Análisis de Imágenes, pp. 9-12, Madrid, Spain, 2013, ISBN: 978-84-695-8332-6.
Links | BibTeX | Tags: Prometeo 2012, TIASA
@inproceedings{k306,
title = {Human-Computer Interaction for Optical Music Recognition tasks},
author = {J. Calvo-Zaragoza and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/306/wsrfai2013_submission_3.pdf},
isbn = {978-84-695-8332-6},
year = {2013},
date = {2013-09-01},
urldate = {2013-09-01},
booktitle = {Actas del III Workshop de Reconocimiento de Formas y Análisis de Imágenes},
pages = {9-12},
address = {Madrid, Spain},
keywords = {Prometeo 2012, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Oncina, J.; Iñesta, J. M.
Recognition of Online Handwritten Music Symbols Proceedings Article
In: Proceedings of the 6th International Workshop on Machine Learning and Music, Prague, Czech Republic, 2013.
Abstract | Links | BibTeX | Tags: Prometeo 2012, TIASA
@inproceedings{k307,
title = {Recognition of Online Handwritten Music Symbols},
author = {J. Calvo-Zaragoza and J. Oncina and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/307/calvozaragoza-mml13.pdf},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {Proceedings of the 6th International Workshop on Machine Learning and Music},
address = {Prague, Czech Republic},
abstract = {An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classifocation results than the others.},
keywords = {Prometeo 2012, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classifocation results than the others.
2013
Calvo-Zaragoza, J.; Oncina, J.
Human-Computer Interaction for Optical Music Recognition tasks Proceedings Article
In: Actas del III Workshop de Reconocimiento de Formas y Análisis de Imágenes, pp. 9-12, Madrid, Spain, 2013, ISBN: 978-84-695-8332-6.
Links | BibTeX | Tags: Prometeo 2012, TIASA
@inproceedings{k306,
title = {Human-Computer Interaction for Optical Music Recognition tasks},
author = {J. Calvo-Zaragoza and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/306/wsrfai2013_submission_3.pdf},
isbn = {978-84-695-8332-6},
year = {2013},
date = {2013-09-01},
urldate = {2013-09-01},
booktitle = {Actas del III Workshop de Reconocimiento de Formas y Análisis de Imágenes},
pages = {9-12},
address = {Madrid, Spain},
keywords = {Prometeo 2012, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Oncina, J.; Iñesta, J. M.
Recognition of Online Handwritten Music Symbols Proceedings Article
In: Proceedings of the 6th International Workshop on Machine Learning and Music, Prague, Czech Republic, 2013.
Abstract | Links | BibTeX | Tags: Prometeo 2012, TIASA
@inproceedings{k307,
title = {Recognition of Online Handwritten Music Symbols},
author = {J. Calvo-Zaragoza and J. Oncina and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/307/calvozaragoza-mml13.pdf},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {Proceedings of the 6th International Workshop on Machine Learning and Music},
address = {Prague, Czech Republic},
abstract = {An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classifocation results than the others.},
keywords = {Prometeo 2012, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classifocation results than the others.