An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio

Ming Liu, Qiyu Sun, Dustin E. Brewer, Thomas M. Gehring, Jesse Eickholt

Research output: Contribution to journalArticlepeer-review

Abstract

Reliable and efficient avian monitoring tools are required to identify population change and then guide conservation initiatives. Autonomous recording units (ARUs) could increase both the amount and quality of monitoring data, though manual analysis of recordings is time consuming. Machine learning could help to analyze these audio data and identify focal species, though few ornithologists know how to cater this tool for their own projects. We present a workflow that exemplifies how machine learning can reduce the amount of expert review time required for analyzing audio recordings to detect a secretive focal species (Sora; Porzana carolina). The deep convolutional neural network that we trained achieved a precision of 97% and reduced the amount of audio for expert review by ~66% while still retaining 60% of Sora calls. Our study could be particularly useful, as an example, for those who wish to utilize machine learning to analyze audio recordings of a focal species that has not often been recorded. Such applications could help to facilitate the effective conservation of avian populations.

Original languageEnglish
Article number3816
JournalRemote Sensing
Volume14
Issue number15
DOIs
StatePublished - Aug 2022

Keywords

  • ARUs
  • Sora
  • avian monitoring
  • deep learning
  • passive acoustic monitoring
  • secretive marsh bird

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