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dc.contributor.authorJia, YK
dc.contributor.authorWu, Z
dc.contributor.authorXu, Y
dc.contributor.authorKe, D
dc.contributor.authorSu, K
dc.date.accessioned2020-10-19T04:27:42Z
dc.date.available2020-10-19T04:27:42Z
dc.date.issued2017
dc.identifier.issn1687-9600
dc.identifier.doi10.1155/2017/2061827
dc.identifier.urihttp://hdl.handle.net/10072/398460
dc.description.abstractLong Short-Term Memory (LSTM) is a kind of Recurrent Neural Networks (RNN) relating to time series, which has achieved good performance in speech recogniton and image recognition. Long Short-Term Memory Projection (LSTMP) is a variant of LSTM to further optimize speed and performance of LSTM by adding a projection layer. As LSTM and LSTMP have performed well in pattern recognition, in this paper, we combine them with Connectionist Temporal Classification (CTC) to study piano’s continuous note recognition for robotics. Based on the Beijing Forestry University music library, we conduct experiments to show recognition rates and numbers of iterations of LSTM with a single layer, LSTMP with a single layer, and Deep LSTM (DLSTM, LSTM with multilayers). As a result, the single layer LSTMP proves performing much better than the single layer LSTM in both time and the recognition rate; that is, LSTMP has fewer parameters and therefore reduces the training time, and, moreover, benefiting from the projection layer, LSTMP has better performance, too. The best recognition rate of LSTMP is . As for DLSTM, the recognition rate can reach because of the effectiveness of the deep structure, but compared with the single layer LSTMP, DLSTM needs more training time.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherHindawi Limited
dc.relation.ispartofpagefrom2061827
dc.relation.ispartofjournalJournal of Robotics
dc.relation.ispartofvolume2017
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleLong Short-Term Memory Projection Recurrent Neural Network Architectures for Piano's Continuous Note Recognition
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationJia, YK; Wu, Z; Xu, Y; Ke, D; Su, K, Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano's Continuous Note Recognition, Journal of Robotics, 2017, 2017, pp. 2061827
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-10-19T02:51:58Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2017 YuKang Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorSu, Kaile


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