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Designing better frog call recognition models

Paul S Crump1, Jeff Houlahan1

  • 1Department of Biology University of New Brunswick-Saint John Saint John NB Canada.

Ecology and Evolution
|May 9, 2017
PubMed
Summary

Optimizing automated sound recognition tools for wildlife monitoring is crucial. Variable settings significantly impact recognizer performance more than training data size, with Type II errors being more consistent for extrapolation.

Keywords:
Lithobates sylvaticusamphibian monitoringautomated acoustic monitoringsong scopesound recognition modelstransferabilitytype I and II errorswood frogs

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Design Example: Frog Muscle Response01:14

Design Example: Frog Muscle Response

A student is tasked to work on an intriguing experiment involving an RL (Resistor-Inductor) circuit to study the muscle response of a frog's leg to electrical stimulation. The RL circuit plays a crucial role in this experiment, providing the means to control and measure the electrical impulses that trigger muscle contraction.
When the switch connecting the RL circuit is closed, a brief muscle contraction is observed. This is because, at a steady state, the inductor acts like a short...
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