Hybrid hidden Markov models and artificial neural networks for handwritten music recognition in mensural notation
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Campo DC | Valor | Idioma |
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dc.contributor | Reconocimiento de Formas e Inteligencia Artificial | es_ES |
dc.contributor.author | Calvo-Zaragoza, Jorge | - |
dc.contributor.author | Toselli, Alejandro H. | - |
dc.contributor.author | Vidal Ruiz, Enrique | - |
dc.contributor.other | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | es_ES |
dc.date.accessioned | 2019-10-03T11:59:05Z | - |
dc.date.available | 2019-10-03T11:59:05Z | - |
dc.date.issued | 2019-11 | - |
dc.identifier.citation | Pattern Analysis and Applications. 2019, 22(4): 1573-1584. doi:10.1007/s10044-019-00807-1 | es_ES |
dc.identifier.issn | 1433-7541 (Print) | - |
dc.identifier.issn | 1433-755X (Online) | - |
dc.identifier.uri | http://hdl.handle.net/10045/97032 | - |
dc.description.abstract | In this paper, we present a hybrid approach using hidden Markov models (HMM) and artificial neural networks to deal with the task of handwritten Music Recognition in mensural notation. Previous works have shown that the task can be addressed with Gaussian density HMMs that can be trained and used in an end-to-end manner, that is, without prior segmentation of the symbols. However, the results achieved using that approach are not sufficiently accurate to be useful in practice. In this work, we hybridize HMMs with deep multilayer perceptrons (MLPs), which lead to remarkable improvements in optical symbol modeling. Moreover, this hybrid architecture maintains important advantages of HMMs such as the ability to properly model variable-length symbol sequences through segmentation-free training, and the simplicity and robustness of combining optical models with N-gram language models, which provide statistical a priori information about regularities in musical symbol concatenation observed in the training data. The results obtained with the proposed hybrid MLP-HMM approach outperform previous works by a wide margin, achieving symbol-level error rates around 26%, as compared with about 40% reported in previous works. | es_ES |
dc.language | eng | es_ES |
dc.publisher | Springer London | es_ES |
dc.rights | © Springer-Verlag London Ltd., part of Springer Nature 2019 | es_ES |
dc.subject | Handwritten music recognition | es_ES |
dc.subject | Mensural notation | es_ES |
dc.subject | Hidden Markov models | es_ES |
dc.subject | Artificial neural networks | es_ES |
dc.subject | N-gram Language Models | es_ES |
dc.subject.other | Lenguajes y Sistemas Informáticos | es_ES |
dc.title | Hybrid hidden Markov models and artificial neural networks for handwritten music recognition in mensural notation | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.peerreviewed | si | es_ES |
dc.identifier.doi | 10.1007/s10044-019-00807-1 | - |
dc.relation.publisherversion | https://doi.org/10.1007/s10044-019-00807-1 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas |
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2019_Calvo-Zaragoza_etal_PatternAnalApplic_final.pdf | Versión final (acceso restringido) | 1,42 MB | Adobe PDF | Abrir Solicitar una copia |
2019_Calvo-Zaragoza_etal_PatternAnalApplic_preprint.pdf | Preprint (acceso abierto) | 2,76 MB | Adobe PDF | Abrir Vista previa |
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