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

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 15; p. 3816
• Main Authors: Liu, Ming, Sun, Qiyu, Brewer, Dustin E., Gehring, Thomas M., Eickholt, Jesse
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2022
• Subjects: Animal populations; Artificial neural networks; ARUs; Audio data; Automation; avian monitoring; Biodiversity; Bird populations; Competition; Conservation; Data analysis; Data collection; Deep learning; Learning algorithms; Machine learning; Monitoring; Neural networks; passive acoustic monitoring; Recording equipment; Remote sensing; secretive marsh bird; Software; Sora; Soundscapes; Species; Wetlands; Workflow
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14153816

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.