Soundscape segregation based on visual analysis and discriminating features

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dc.check.date2022-11-04
dc.contributor.authorDias, Fábio Felix
dc.contributor.authorPedrini, Helio
dc.contributor.authorMinghim, Rosane
dc.contributor.funderCoordenação de Aperfeiçoamento de Pessoal de Nível Superioren
dc.contributor.funderConselho Nacional de Desenvolvimento CientĂ­fico e TecnolĂłgicoen
dc.contributor.funderFundação de Amparo à Pesquisa do Estado de São Pauloen
dc.date.accessioned2021-02-05T13:18:08Z
dc.date.available2021-02-05T13:18:08Z
dc.date.issued2021-11-04
dc.date.updated2021-02-05T12:19:43Z
dc.description.abstractThe distinction of landscapes based on their sound patterns is useful for several analyses. For instance, comparisons of audio files from different periods enable the detection of changes over time in a particular habitat, signaling events of importance, such as modifications in the balance between species and presence of new ones. The handling of a large number of different sound recordings in wild environments also reduces the set of sounds to be examined. However, the current efforts towards soundscape interpretation do not provide enough elements for researchers to automatically split soundscape datasets with degrees of similarity, thus requiring users' feedback for the grouping of highly related recordings. This work introduces a strategy for the exploration and analysis of soundscapes that highlights data characteristics related to differences and similarities among distinct soundscapes. It is based on a visual and numerical evaluation of feature spaces and was applied to three feature sets, namely acoustic indices and measurements, images from audio spectrograms depicted by classic features, and the same images depicted by features automatically generated by Deep Learning techniques. The results indicate that certain combinations of acoustic indices and measurements perform well for the discrimination task, although other feature sets have not been discarded. In addition, visual techniques were able to assist this type of analysis.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid101184en
dc.identifier.citationDias, F. F., Pedrini, H. and Minghim, R. (2021) 'Soundscape segregation based on visual analysis and discriminating features', Ecological Informatics, 61, 101184 (13 pp). doi: 10.1016/j.ecoinf.2020.101184en
dc.identifier.doi10.1016/j.ecoinf.2020.101184en
dc.identifier.endpage13en
dc.identifier.issn1574-9541
dc.identifier.journaltitleEcological Informaticsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/11042
dc.identifier.volume61en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1574954120301345
dc.rights© 2020 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectAcoustic featuresen
dc.subjectDeep learningen
dc.subjectImage descriptorsen
dc.subjectInformation visualizationen
dc.subjectSpectrogram imageen
dc.titleSoundscape segregation based on visual analysis and discriminating featuresen
dc.typeArticle (peer-reviewed)en
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