Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/20250
DC FieldValueLanguage
dc.contributor.authorNoda, Juan J.en_US
dc.contributor.authorTravieso-González, Carlos M.en_US
dc.contributor.authorSánchez-Rodríguez, Daviden_US
dc.date.accessioned2017-02-10T03:30:24Z-
dc.date.accessioned2018-03-16T09:13:34Z-
dc.date.available2017-02-10T03:30:24Z-
dc.date.available2018-03-16T09:13:34Z-
dc.date.issued2016en_US
dc.identifier.issn2076-3417en_US
dc.identifier.urihttp://hdl.handle.net/10553/20250-
dc.description.abstractFish as well as birds, mammals, insects and other animals are capable of emitting sounds for diverse purposes, which can be recorded through microphone sensors. Although fish vocalizations have been known for a long time, they have been poorly studied and applied in their taxonomic classification. This work presents a novel approach for automatic remote acoustic identification of fish through their acoustic signals by applying pattern recognition techniques. The sound signals are preprocessed and automatically segmented to extract each call from the background noise. Then, the calls are parameterized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC), Shannon Entropy (SE) and Syllable Length (SL), yielding useful information for the classification phase. In our experiments, 102 different fish species have been successfully identified with three widely used machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 95.24%, 93.56% and 95.58%, respectively.en_US
dc.formatapplication/pdfes
dc.languageengen_US
dc.relation.ispartofApplied Sciences (Basel)en_US
dc.rightsbyes
dc.sourceApplied Sciences Basel [ISSN 2076-3417], v. 6 (12), p. 443en_US
dc.subject2401 Biología animal (zoología)en_US
dc.subject.otherBiological acoustic analysisen_US
dc.subject.otherBioacoustic taxonomy identificationen_US
dc.subject.otherFish acoustic signalen_US
dc.subject.otherHydroacoustic sensorsen_US
dc.subject.otherSpecies mappingen_US
dc.titleAutomatic taxonomic classification of fish based on their acoustic signalsen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/app6120443
dc.identifier.scopus85007486898
dc.identifier.isi000391626800018-
dc.contributor.authorscopusid57187964500
dc.contributor.authorscopusid6602376272
dc.contributor.authorscopusid56690271600
dc.identifier.crisid-;2587;3008-
dc.identifier.eissn1454-5101-
dc.investigacionCienciasen_US
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.type2Artículoen_US
dc.contributor.daisngid7100305
dc.contributor.daisngid265761
dc.contributor.daisngid3316951
dc.contributor.wosstandardWOS:Noda, JJ
dc.contributor.wosstandardWOS:Travieso, CM
dc.contributor.wosstandardWOS:Sanchez-Rodriguez, D
dc.date.coverdateEnero 2016
dc.identifier.ulpgces
dc.description.jcr1,679
dc.description.jcrqQ3
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon 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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