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.

中文翻译：

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通过脉冲重复率自动检测青蛙叫声和合唱声

Anurans（青蛙和蟾蜍）是全球受威胁最严重的分类群之一。无尾目动物的成功保护将依赖于当地种群状况和变化的改进数据，尤其是稀有和受威胁物种。自动传感器，如录音机，有可能通过大规模增加人口抽样工作的空间和时间尺度来提供此类数据。分析此类数据集需要强大而高效的工具，可以自动识别音频记录中某个物种的存在。像蝙蝠和鸟类一样，许多无尾猿物种会发出独特的声音，这些声音可以被自主录音机捕捉到，并且是自动识别的绝佳候选者。然而，与鸟类和蝙蝠相比，用于无尾目动物的有效自动声学识别工具尚未广泛使用。一种有效的无尾目动物呼叫识别方法必须能够应对现实世界现场数据的挑战，并且不需要大量的标记数据集。我们设计了一种发声识别工具，可根据其周期性结构对录音中的无尾猿发声进行分类：基于重复间隔的生物声学识别工具 (RIBBIT)。我们将 RIBBIT 应用于野外录音以研究北方合唱青蛙（基于重复间隔的生物声学识别工具 (RIBBIT)。我们将 RIBBIT 应用于野外录音以研究北方合唱青蛙（基于重复间隔的生物声学识别工具 (RIBBIT)。我们将 RIBBIT 应用于野外录音以研究北方合唱青蛙（Pseudacris maculata ) 温带北美草原和极度濒危的可变丑蛙 ( Atelopus varius ) 热带中美洲热带雨林。该工具准确地识别了北方合唱青蛙，即使它们以严重重叠的合唱发声，并在视觉调查中很少遇到的野外现场识别出可变的丑角青蛙发声。使用一些简单的参数，RIBBIT 可以检测任何具有周期性结构的发声，包括许多无尾目、昆虫、鸟类和哺乳动物的发声。我们在 Python 和 R 中提供 RIBBIT 的开源实现，以支持其在其他分类群和社区中的使用。