Comparison of Full-Spectrum and Zero-Crossing Automated Bat Call Classifiers Donald Solick, Matthew...
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![Page 1: Comparison of Full-Spectrum and Zero-Crossing Automated Bat Call Classifiers Donald Solick, Matthew Clement, Kevin Murray, Christopher Nations, and Jeffery.](https://reader035.fdocuments.net/reader035/viewer/2022062421/56649cfd5503460f949cdcbe/html5/thumbnails/1.jpg)
Comparison of Full-Spectrum and Zero-Crossing Automated Bat Call
Classifiers
Donald Solick, Matthew Clement, Kevin Murray, Christopher Nations, and Jeffery Gruver
Western EcoSystems Technology (WEST), Inc.
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![Page 3: Comparison of Full-Spectrum and Zero-Crossing Automated Bat Call Classifiers Donald Solick, Matthew Clement, Kevin Murray, Christopher Nations, and Jeffery.](https://reader035.fdocuments.net/reader035/viewer/2022062421/56649cfd5503460f949cdcbe/html5/thumbnails/3.jpg)
Full-Spectrum (FS)
Time and Frequency
Amplitude
Multiple frequency content
-harmonics, multiple bats, calls
against background noise
Zero-Crossing (ZC)
Time and Frequency
Dominant frequency content
-loudest sound gets recorded
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Objective
To determine which type of classifier is
better at species discrimination given
the same set of known calls
Assumption
More information = better
species discrimination?
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FS Reference CallsSpecies Abbreviation Number of files
Perimyotis subflavus PESU 97
Lasiurus borealis LABO 40
Eptesicus fuscus EPFU 115
Lasionycteris noctivagans LANO 23
Lasiurus cinereus LACI 18
Myotis leibii MYLE 23
Myotis septentrionalis MYSE 46
Myotis lucifugus MYLU 263
Myotis sodalis MYSO 26
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SonoBat 3.04 Northeast
Mean Classification: average of all calls in file
By Vote: majority of calls in file
Consensus: when Class and Vote agree
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SonoBat 3.04 Northeast
Mean Classification: average of all calls in file
By Vote: majority of calls in file
Consensus: when Class and Vote agree
x = Lano Lano 0.9991
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SonoBat 3.04 Northeast
Mean Classification: average of all calls in file
By Vote: majority of calls in file
Consensus: when Class and Vote agree
Lano Lano LanoEpfu Lano
Lano 4 of 5
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SonoBat 3.04 Northeast
Mean Classification: average of all calls in file
By Vote: majority of calls in file
Consensus: when Class and Vote agree
Lano 4 of 5
Lano 0.9991Lano
![Page 10: Comparison of Full-Spectrum and Zero-Crossing Automated Bat Call Classifiers Donald Solick, Matthew Clement, Kevin Murray, Christopher Nations, and Jeffery.](https://reader035.fdocuments.net/reader035/viewer/2022062421/56649cfd5503460f949cdcbe/html5/thumbnails/10.jpg)
ZC Classifiers
Bat Classification and Identification (BCID) East v2.4mAC
www.batcallid.com
EchoClass 64 v1
www.fws.gov/midwest/Endangered/mammals/inba/
inbasummersurveyguidance.html
Discriminant Function Analysis for New York
Developed by Eric Britzke for use by NY Dept.of Environmental
Conservation
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Anabat Converter 0.8 (http://bertrik.sikken.nl/anabat/)
AnalookW 3.8e
Applied filter and extracted parameters
Except EchoClass
Converting FS to ZC
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SonoBat
BCIDEcho Class NY DFA Class Vote Consensus
% Correct 49 56 45 53 43 56
% Incorrect 6 7 4 45 38 36
% Unknown 43 14 49 2 15 0
Overall Classification Rates (%)
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SonoBat
BCIDEcho Class NY DFA Class Vote Consensus
% Correct 49 56 45 53 43 56
% Incorrect 6 7 4 45 38 36
% Unknown 43 14 49 2 15 0
Overall Classification Rates (%)
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Species
SonoBat
BCID Echo Class NY DFA Class Vote Consensus
PESU 96 98 96 98 77 95
LABO 43 70 30 60 65 63
EPFU 88 93 86 51 57 78
LANO 74 83 74 52 26 65
LACI 53 68 53 53 58 63
MYLE 26 26 17 9 0 4
MYSE 33 33 26 57 46 46
MYLU 31 33 24 56 25 56
MYSO 0 0 0 38 31 35
Correct Classification Rates (% Correct)
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Species
SonoBat
BCID Echo Class NY DFA Class Vote Consensus
PESU 96 98 96 98 77 95
LABO 43 70 30 60 65 63
EPFU 88 93 86 51 57 78
LANO 74 83 74 52 26 65
LACI 53 68 53 53 58 63
MYLE 26 26 17 9 0 4
MYSE 33 33 26 57 46 46
MYLU 31 33 24 56 25 56
MYSO 0 0 0 38 31 35
Correct Classification Rates (% Correct)
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Correct Classification Rates (% Correct)
Species
SonoBat
BCID Echo Class NY DFA Class Vote Consensus
PESU 96 98 96 98 77 95
LABO 43 70 30 60 65 63
EPFU 88 93 86 51 57 78
LANO 74 83 74 52 26 65
LACI 53 68 53 53 58 63
MYLE 26 26 17 9 0 4
MYSE 33 33 26 57 46 46
MYLU 31 33 24 56 25 56
MYSO 0 0 0 38 31 35
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Summary
• None of the classifiers performed well overall
• SonoBat better for non-Myotis, ZC better for
Myotis
• SonoBat more conservative
• Caution when using automated classification
for Indiana bat surveys
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Caveats
• Low sample size for some species
• Lost in translation?
• Different Analook filters could improve or
worsen ZC classifier performance
• Focus on trends, not absolute comparisons
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Conclusion
• Illustrates limitations of automated
classification of bat acoustic data
• Species presence/probable absence should be
based on multiple lines of evidence
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Thank You!
Ryan Allen, Bat Call Identification, Inc.
Eric Britzke, US Army Engineer Research & Development Center
John Chenger, Bat Conservation and Management
Carl Herzog, New York Dept. of Environmental Conservation
Amie Shovlain, Montana Natural Heritage Program
Craig Stihler, West Virginia Dept. of Natural Resources
Joe Szewczak, Humboldt State University