Visualization and categorization of ecological acoustic events based on discriminant features

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversity College Cork-
Autor(es): dc.creatorHuancapaza Hilasaca, Liz Maribel-
Autor(es): dc.creatorGaspar, Lucas Pacciullio [UNESP]-
Autor(es): dc.creatorRibeiro, Milton Cezar [UNESP]-
Autor(es): dc.creatorMinghim, Rosane-
Data de aceite: dc.date.accessioned2022-02-22T00:53:10Z-
Data de disponibilização: dc.date.available2022-02-22T00:53:10Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.ecolind.2020.107316-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/208347-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/208347-
Descrição: dc.descriptionAlthough sound classification in soundscape studies are generally performed by experts, the large growth of acoustic data presents a major challenge for performing such task. At the same time, the identification of more discriminating features becomes crucial when analyzing soundscapes, and this occurs because natural and anthropogenic sounds are very complex, particularly in Neotropical regions, where the biodiversity level is very high. In this scenario, the need for research addressing the discriminatory capability of acoustic features is of utmost importance to work towards automating these processes. In this study we present a method to identify the most discriminant features for categorizing sound events in soundscapes. Such identification is key to classification of sound events. Our experimental findings validate our method, showing high discriminatory capability of certain extracted features from sound data, reaching an accuracy of 89.91% for classification of frogs, birds and insects simultaneously. An extension of these experiments to simulate binary classification reached accuracy of 82.64%,100.0% and 99.40% for the classification between combinations of frogs-birds, frogs-insects and birds-insects, respectively.-
Descrição: dc.descriptionInstituto de Ciências Matemáticas e de Computação (ICMC) University of São Paulo-
Descrição: dc.descriptionDepartment of Biodiversity São Paulo State University - UNESP-
Descrição: dc.descriptionSchool of Computer Science and Information Technology University College Cork-
Descrição: dc.descriptionDepartment of Biodiversity São Paulo State University - UNESP-
Idioma: dc.languageen-
Relação: dc.relationEcological Indicators-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectDiscriminant features-
Palavras-chave: dc.subjectFeature selection-
Palavras-chave: dc.subjectSoundscape ecology-
Palavras-chave: dc.subjectVisualization-
Título: dc.titleVisualization and categorization of ecological acoustic events based on discriminant features-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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