Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/52591
DC FieldValueLanguage
dc.contributor.authorNoda, Juan J.en_US
dc.contributor.authorTravieso, Carlos M.en_US
dc.contributor.authorSanchez-Rodriguez, Daviden_US
dc.contributor.otherSanchez, David
dc.contributor.otherTravieso-Gonzalez, Carlos M.
dc.date.accessioned2018-12-04T10:04:36Z-
dc.date.available2018-12-04T10:04:36Z-
dc.date.issued2016en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://hdl.handle.net/10553/52591-
dc.description.abstractThe automatic recognition of anurans by their calls provides indicators of ecosystem health and habitat quality. This paper presents a new methodology for the acoustic classification of anurans using a fusion of frequency domain features, Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs), with time domain features like entropy and syllable duration through intelligent systems. This methodology has been validated in three databases with a significant number of different species proving the strength of this approach. First, the audio recordings are automatically segmented into syllables which represent different anuran calls. For each syllable, both types of features are computed and evaluated separately as in previous works. In the experiments, a novel data fusion method has been used showing an increase of the classification accuracy which achieves an average of 98.80% ± 2.43 in 41 anuran species from AmphibiaWeb database, 96.90% ± 3.57 in 58 frogs from Cuba and 95.48% ± 4.97 in 100 anurans from southern Brazil and Uruguay; reaching a classification rate of 95.38% ± 5.05 for the aggregate dataset of 199 species.en_US
dc.languageengen_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.sourceExpert Systems With Applications[ISSN 0957-4174],v. 50, p. 100-106en_US
dc.subject240601 Bioacústicaen_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherAcoustic data fusionen_US
dc.subject.otherBioacoustic taxonomy identificationen_US
dc.subject.otherBiological acoustic analysisen_US
dc.subject.otherSVMen_US
dc.titleMethodology for automatic bioacoustic classification of anurans based on feature fusionen_US
dc.typeinfo:eu-repo/semantics/Articlees
dc.typeArticlees
dc.identifier.doi10.1016/j.eswa.2015.12.020
dc.identifier.scopus84961303335
dc.identifier.isi000370305000009
dcterms.isPartOfExpert Systems With Applications
dcterms.sourceExpert Systems With Applications[ISSN 0957-4174],v. 50, p. 100-106
dc.contributor.authorscopusid57187964500
dc.contributor.authorscopusid6602376272
dc.contributor.authorscopusid56690271600
dc.description.lastpage106-
dc.description.firstpage100-
dc.relation.volume50-
dc.investigacionCienciasen_US
dc.type2Artículoen_US
dc.identifier.wosWOS:000370305000009
dc.contributor.daisngid7100305
dc.contributor.daisngid265761
dc.contributor.daisngid3316951
dc.identifier.investigatorRIDB-4519-2010
dc.identifier.investigatorRIDNo ID
dc.contributor.wosstandardWOS:Noda, JJ
dc.contributor.wosstandardWOS:Travieso, CM
dc.contributor.wosstandardWOS:Sanchez-Rodriguez, D
dc.date.coverdateMayo 2016
dc.identifier.ulpgces
dc.description.sjr1,433
dc.description.jcr3,928
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptGIR IDeTIC: División de Redes y Servicios Telemáticos-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Ingeniería Telemática-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.orcid0000-0003-2700-1591-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
crisitem.author.fullNameSánchez Rodríguez, David De La Cruz-
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