Next Article in Journal
Single-Epoch Ambiguity Resolution of a Large-Scale CORS Network with Multi-Frequency and Multi-Constellation GNSS
Previous Article in Journal
Data-Free Area Detection and Evaluation for Marine Satellite Data Products
Previous Article in Special Issue
A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds
 
 
Technical Note

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

1
Department of Computer Science, Central Michigan University, Mt. Pleasant, MI 48859, USA
2
Department of Biology, Institute for Great Lakes Research, Central Michigan University, Mt. Pleasant, MI 48859, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Stuart Phinn
Remote Sens. 2022, 14(15), 3816; https://doi.org/10.3390/rs14153816
Received: 16 June 2022 / Revised: 27 July 2022 / Accepted: 3 August 2022 / Published: 8 August 2022
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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. View Full-Text
Keywords: ARUs; secretive marsh bird; Sora; deep learning; avian monitoring; passive acoustic monitoring ARUs; secretive marsh bird; Sora; deep learning; avian monitoring; passive acoustic monitoring
Show Figures

Figure 1

MDPI and ACS Style

Liu, M.; Sun, Q.; Brewer, D.E.; Gehring, T.M.; Eickholt, J. An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio. Remote Sens. 2022, 14, 3816. https://doi.org/10.3390/rs14153816

AMA Style

Liu M, Sun Q, Brewer DE, Gehring TM, Eickholt J. An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio. Remote Sensing. 2022; 14(15):3816. https://doi.org/10.3390/rs14153816

Chicago/Turabian Style

Liu, Ming, Qiyu Sun, Dustin E. Brewer, Thomas M. Gehring, and Jesse Eickholt. 2022. "An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio" Remote Sensing 14, no. 15: 3816. https://doi.org/10.3390/rs14153816

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop