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Automated detection of frog calls and choruses by pulse repetition rate
Authors:Sam Lapp  Tianhao Wu  Corinne Richards-Zawacki  Jamie Voyles  Keely Michelle Rodriguez  Hila Shamon  Justin Kitzes
Institution:1. Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA;2. Department of Biology, University of Nevada, Reno, Reno, Nevada, USA;3. Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, Virginia, USA
Abstract: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.
Keywords:acoustic  amphibian  classification  detection  endangered  machine learning  monitoring  signal processing  acústico  anfibio  aprendizaje mecánico  clasificación  detección  en peligro  monitoreo  procesamiento de señal
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