Classification of optical music symbols based on combined neural network
Classification of optical music symbols based on combined neural network
dc.contributor.author | Wen,C | en |
dc.contributor.author | Ana Maria Rebelo | en |
dc.contributor.author | Zhang,J | en |
dc.contributor.author | Jaime Cardoso | en |
dc.date.accessioned | 2018-01-21T21:13:36Z | |
dc.date.available | 2018-01-21T21:13:36Z | |
dc.date.issued | 2014 | en |
dc.description.abstract | In this paper, a new method for music symbol classification named Combined Neural Network (CNN) is proposed. Tests are conducted on more than 9000 music symbols from both real and scanned music sheets, which show that the proposed technique offers superior classification capability. At the same time, the performance of the new network is compared with the single Neural Network (NN) classifier using the same music scores. The average classification accuracy increased more than ten percent, reaching 98.82%. © 2014 IEEE. | en |
dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/7205 | |
dc.identifier.uri | http://dx.doi.org/10.1109/icmc.2014.7231590 | en |
dc.language | eng | en |
dc.relation | 3889 | en |
dc.relation | 4884 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.title | Classification of optical music symbols based on combined neural network | en |
dc.type | conferenceObject | en |
dc.type | Publication | en |
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