dc.creator | Luque Sendra, Amalia | es |
dc.creator | Romero-Lemos, Javier | es |
dc.creator | Carrasco Muñoz, Alejandro | es |
dc.creator | González Abril, Luis | es |
dc.date.accessioned | 2018-07-09T07:33:02Z | |
dc.date.available | 2018-07-09T07:33:02Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Luque Sendra, A., Romero-Lemos, J., Carrasco Muñoz, A. y González Abril, L. (2018). Temporally-aware algorithms for the classification of anuran sounds. PeerJ, 6, e4732-. | |
dc.identifier.issn | 2167-8359 | es |
dc.identifier.uri | https://hdl.handle.net/11441/76992 | |
dc.description.abstract | Several authors have shown that the sounds of anurans can be used as an indicator of
climate change. Hence, the recording, storage and further processing of a huge
number of anuran sounds, distributed over time and space, are required in order to
obtain this indicator. Furthermore, it is desirable to have algorithms and tools for
the automatic classification of the different classes of sounds. In this paper, six
classification methods are proposed, all based on the data-mining domain, which
strive to take advantage of the temporal character of the sounds. The definition and
comparison of these classification methods is undertaken using several approaches.
The main conclusions of this paper are that: (i) the sliding window method attained
the best results in the experiments presented, and even outperformed the hidden
Markov models usually employed in similar applications; (ii) noteworthy overall
classification performance has been obtained, which is an especially striking result
considering that the sounds analysed were affected by a highly noisy background;
(iii) the instance selection for the determination of the sounds in the training dataset
offers better results than cross-validation techniques; and (iv) the temporally-aware
classifiers have revealed that they can obtain better performance than their nontemporally-aware
counterparts. | es |
dc.description.sponsorship | Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain): excellence eSAPIENS number TIC 5705 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | PeerJ | es |
dc.relation.ispartof | PeerJ, 6, e4732-. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Global warming | es |
dc.subject | Sound classification | es |
dc.subject | Data mining | es |
dc.subject | Feature extraction | es |
dc.subject | Machine learning | es |
dc.subject | Habitat monitoring | es |
dc.title | Temporally-aware algorithms for the classification of anuran sounds | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería del Diseño | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Tecnología Electrónica | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Economía Aplicada I | es |
dc.relation.projectID | TIC 5705 | es |
dc.relation.publisherversion | https://peerj.com/articles/4732/ | es |
dc.identifier.doi | 10.7717/peerj.4732 | es |
dc.contributor.group | Universidad de Sevilla. TEP022: Diseño Industrial e Ingeniería del Proyecto y la Innovación | es |
dc.contributor.group | Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial | es |
dc.contributor.group | Universidad de Sevilla. SEJ442: Métodos Cualitativos y Optimización en Sistemas Dinámicos Económicos | es |
idus.format.extent | 40 p. | es |
idus.validador.nota | Mejor artículo científico del mes de mayo 2018 en EPS | es |
dc.journaltitle | PeerJ | es |
dc.publication.issue | 6 | es |
dc.publication.initialPage | e4732 | es |
dc.contributor.funder | Junta de Andalucía | |