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dc.creatorSilva, Marielle Jordane da-
dc.creatorCarrillo Melgarejo, Dick-
dc.creatorRosa, Renata Lopes-
dc.creatorZegarra Rodríguez, Demóstenes-
dc.date.accessioned2020-08-14T19:00:03Z-
dc.date.available2020-08-14T19:00:03Z-
dc.date.issued2020-03-
dc.identifier.citationSILVA, M. J. da et al. Speech quality classifier model based on DBN that considers atmospheric phenomena. Journal of Communications Software and Systems, Split, v. 16, n. 1, p. 75-84, Mar. 2020. DOI: http://dx.doi.org/10.24138/jcomss.v16i1.1033.pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/42434-
dc.description.abstractCurrent implementations of 5G networks consider higher frequency range of operation than previous telecommunication networks, and it is possible to offer higher data rates for different applications. On the other hand, atmospheric phenomena could have a more negative impact on the transmission quality. Thus, the study of the transmitted signal quality at high frequencies is relevant to guaranty the user ́s quality of experience. In this research, the recommendations ITU-R P.838-3 and ITU-R P.676-11 are implemented in a network scenario, which are methodologies to estimate the signal degradations originated by rainfall and atmospheric gases, respectively. Thus, speech signals are encoded by the AMR-WB codec, transmitted and the perceptual speech quality is evaluated using the algorithm described in ITU-T Rec. P.863, mostly known as POLQA. The novelty of this work is to propose a non-intrusive speech quality classifier that considers atmospheric phenomena. This classifier is based on Deep Belief Networks (DBN) that uses Support Vector Machine (SVM) with radial basis function kernel (RBF-SVM) as classifier, to identify five predefined speech quality classes. Experimental Results show that the proposed speech quality classifier reached an accuracy between 92% and 95% for each quality class overcoming the results obtained by the sole non-intrusive standard described in ITU-T Recommendation P.563. Furthermore, subjective tests are carried out to validate the proposed classifier performance, and it reached an accuracy of 94.8%.pt_BR
dc.languageenpt_BR
dc.publisherUniversity of Split, FESBpt_BR
dc.rightsacesso abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.sourceJournal of Communications Software and Systemspt_BR
dc.subjectWireless communicationspt_BR
dc.subjectSpeech qualitypt_BR
dc.subjectAtmospheric phenomenapt_BR
dc.subjectRainpt_BR
dc.subjectAtmospheric gasespt_BR
dc.subjectComunicações sem fiopt_BR
dc.subjectVoz - Qualidadept_BR
dc.subjectFenômenos atmosféricospt_BR
dc.subjectChuvapt_BR
dc.subjectGases atmosféricospt_BR
dc.titleSpeech quality classifier model based on DBN that considers atmospheric phenomenapt_BR
dc.typeArtigopt_BR
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