Automated detection of frog calls and choruses by pulse repetition rate

© 2021 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.

Bibliographische Detailangaben
Veröffentlicht in:Conservation biology : the journal of the Society for Conservation Biology. - 1989. - 35(2021), 5 vom: 07. Okt., Seite 1659-1668
1. Verfasser: Lapp, Sam (VerfasserIn)
Weitere Verfasser: Wu, Tianhao, Richards-Zawacki, Corinne, Voyles, Jamie, Rodriguez, Keely Michelle, Shamon, Hila, Kitzes, Justin
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2021
Zugriff auf das übergeordnete Werk:Conservation biology : the journal of the Society for Conservation Biology
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. acoustic acústico amphibian anfibio aprendizaje mecánico clasificación classification mehr... detección detection en peligro endangered machine learning monitoreo monitoring procesamiento de señal signal processing
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520 |a © 2021 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology. 
520 |a Anurans (frogs and toads) are among the most globally threatened taxonomic groups. Successful conservation of anurans will rely on improved data on the status and changes in local populations, particularly for rare and threatened species. Automated sensors, such as acoustic recorders, have the potential to provide such data by massively increasing the spatial and temporal scale of population sampling efforts. Analyzing such data sets will require robust and efficient tools that can automatically identify the presence of a species in audio recordings. Like bats and birds, many anuran species produce distinct vocalizations that can be captured by autonomous acoustic recorders and represent excellent candidates for automated recognition. However, in contrast to birds and bats, effective automated acoustic recognition tools for anurans are not yet widely available. An effective automated call-recognition method for anurans must be robust to the challenges of real-world field data and should not require extensive labeled data sets. We devised a vocalization identification tool that classifies anuran vocalizations in audio recordings based on their periodic structure: the repeat interval-based bioacoustic identification tool (RIBBIT). We applied RIBBIT to field recordings to study the boreal chorus frog (Pseudacris maculata) of temperate North American grasslands and the critically endangered variable harlequin frog (Atelopus varius) of tropical Central American rainforests. The tool accurately identified boreal chorus frogs, even when they vocalized in heavily overlapping choruses and identified variable harlequin frog vocalizations at a field site where it had been very rarely encountered in visual surveys. Using a few simple parameters, RIBBIT can detect any vocalization with a periodic structure, including those of many anurans, insects, birds, and mammals. We provide open-source implementations of RIBBIT in Python and R to support its use for other taxa and communities 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
650 4 |a acoustic 
650 4 |a acústico 
650 4 |a amphibian 
650 4 |a anfibio 
650 4 |a aprendizaje mecánico 
650 4 |a clasificación 
650 4 |a classification 
650 4 |a detección 
650 4 |a detection 
650 4 |a en peligro 
650 4 |a endangered 
650 4 |a machine learning 
650 4 |a monitoreo 
650 4 |a monitoring 
650 4 |a procesamiento de señal 
650 4 |a signal processing 
700 1 |a Wu, Tianhao  |e verfasserin  |4 aut 
700 1 |a Richards-Zawacki, Corinne  |e verfasserin  |4 aut 
700 1 |a Voyles, Jamie  |e verfasserin  |4 aut 
700 1 |a Rodriguez, Keely Michelle  |e verfasserin  |4 aut 
700 1 |a Shamon, Hila  |e verfasserin  |4 aut 
700 1 |a Kitzes, Justin  |e verfasserin  |4 aut 
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773 1 8 |g volume:35  |g year:2021  |g number:5  |g day:07  |g month:10  |g pages:1659-1668 
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