Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment

Bats have an important role in the ecosystem, and therefore an effective detection of their prevalence can contribute to their conservation. At present, the most commonly methodology used in the study of bats is the analysis of echolocation calls. However, many other ultrasound signals can be simult...

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Autores: Bertran, Marta, Alsina-Pagès, Rosa Ma, Tena, Elena
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Recursos:Universitat Ramon Llull (URL)
Repositorio:DAU Arxiu Digital de la Universitat Ramon Llull
OAI Identifier:oai:dau.url.edu:20.500.14342/3423
Acesso em linha:http://hdl.handle.net/20.500.14342/3423
https://doi.org/10.3390/app9173467
Access Level:acceso abierto
Palavra-chave:Ultrasons en biologia
Ratapinyades
59
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spelling Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real EnvironmentBertran, MartaAlsina-Pagès, Rosa MaTena, ElenaUltrasons en biologiaRatapinyades59Bats have an important role in the ecosystem, and therefore an effective detection of their prevalence can contribute to their conservation. At present, the most commonly methodology used in the study of bats is the analysis of echolocation calls. However, many other ultrasound signals can be simultaneously recorded, and this makes species location and identification a long and difficult task. This field of research could be greatly improved through the use of bioacoustics which provide a more accurate automated detection, identification and count of the wildlife of a particular area. We have analyzed the calls of two bat species—Pipistrellus pipistrellus and Pipistrellus pygmaeus—both of which are common types of bats frequently found in the Iberian Peninsula. These two cryptic species are difficult to identify by their morphological features, but are more easily identified by their echolocation calls. The real-life audio files have been obtained by an Echo Meter Touch Pro 1 bat detector. Time-expanded recordings of calls were first classified manually by means of their frequency, duration and interpulse interval. In this paper, we first detail the creation of a dataset with three classes, which are the two bat species but also the silent intervals. This dataset can be useful to work in mixed species environment. Afterwards, two automatic bat detection and identification machine learning approaches are described, in a laboratory environment, which represent the previous step to real-life in an urban scenario. The priority in that approaches design is the identification using short window analysis in order to detect each bat pulse. However, given that we are concerned with the risks of automatic identification, the main aim of the project is to accelerate the manual ID process for the specialists in the field. The dataset provided will help researchers develop automatic recognition systems for a more accurate identification of the bat species in a laboratory environment, and in a near future, in an urban environment, where those two bat species are common.MDPIUniversitat Ramon Llull. La SalleUniversidad Complutense de Madrid202020232020202320192019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion21 p.application/pdfhttp://hdl.handle.net/20.500.14342/3423https://doi.org/10.3390/app9173467RECERCAT (Dipòsit de la Recerca de Catalunya)reponame:DAU Arxiu Digital de la Universitat Ramon Llullinstname:Universitat Ramon Llull (URL)InglésApplied Sciences. 2019. Vol. 9 ,No. 17Attribution 4.0 International© L'autor/ahttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dau.url.edu:20.500.14342/34232026-06-21T06:40:37Z
dc.title.none.fl_str_mv Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment
title Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment
spellingShingle Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment
Bertran, Marta
Ultrasons en biologia
Ratapinyades
59
title_short Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment
title_full Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment
title_fullStr Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment
title_full_unstemmed Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment
title_sort Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment
dc.creator.none.fl_str_mv Bertran, Marta
Alsina-Pagès, Rosa Ma
Tena, Elena
author Bertran, Marta
author_facet Bertran, Marta
Alsina-Pagès, Rosa Ma
Tena, Elena
author_role author
author2 Alsina-Pagès, Rosa Ma
Tena, Elena
author2_role author
author
dc.contributor.none.fl_str_mv Universitat Ramon Llull. La Salle
Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Ultrasons en biologia
Ratapinyades
59
topic Ultrasons en biologia
Ratapinyades
59
description Bats have an important role in the ecosystem, and therefore an effective detection of their prevalence can contribute to their conservation. At present, the most commonly methodology used in the study of bats is the analysis of echolocation calls. However, many other ultrasound signals can be simultaneously recorded, and this makes species location and identification a long and difficult task. This field of research could be greatly improved through the use of bioacoustics which provide a more accurate automated detection, identification and count of the wildlife of a particular area. We have analyzed the calls of two bat species—Pipistrellus pipistrellus and Pipistrellus pygmaeus—both of which are common types of bats frequently found in the Iberian Peninsula. These two cryptic species are difficult to identify by their morphological features, but are more easily identified by their echolocation calls. The real-life audio files have been obtained by an Echo Meter Touch Pro 1 bat detector. Time-expanded recordings of calls were first classified manually by means of their frequency, duration and interpulse interval. In this paper, we first detail the creation of a dataset with three classes, which are the two bat species but also the silent intervals. This dataset can be useful to work in mixed species environment. Afterwards, two automatic bat detection and identification machine learning approaches are described, in a laboratory environment, which represent the previous step to real-life in an urban scenario. The priority in that approaches design is the identification using short window analysis in order to detect each bat pulse. However, given that we are concerned with the risks of automatic identification, the main aim of the project is to accelerate the manual ID process for the specialists in the field. The dataset provided will help researchers develop automatic recognition systems for a more accurate identification of the bat species in a laboratory environment, and in a near future, in an urban environment, where those two bat species are common.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019
2020
2020
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.14342/3423
https://doi.org/10.3390/app9173467
url http://hdl.handle.net/20.500.14342/3423
https://doi.org/10.3390/app9173467
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Applied Sciences. 2019. Vol. 9 ,No. 17
dc.rights.none.fl_str_mv Attribution 4.0 International
© L'autor/a
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
© L'autor/a
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 21 p.
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv RECERCAT (Dipòsit de la Recerca de Catalunya)
reponame:DAU Arxiu Digital de la Universitat Ramon Llull
instname:Universitat Ramon Llull (URL)
instname_str Universitat Ramon Llull (URL)
reponame_str DAU Arxiu Digital de la Universitat Ramon Llull
collection DAU Arxiu Digital de la Universitat Ramon Llull
repository.name.fl_str_mv
repository.mail.fl_str_mv
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