Journal
ECOLOGICAL INFORMATICS
Volume 21, Issue -, Pages 34-39Publisher
ELSEVIER
DOI: 10.1016/j.ecoinf.2013.12.002
Keywords
Nocturnal flight call; Machine learning; Random Forest; Bioacoustics; Workflow
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Funding
- Kenneth L. Harder Trust
- Scott and Karen Harder Family
- NYSERDA
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Passive acoustic monitoring often leads to large quantities of sound data which are burdensome to process, such that the availability and cost of expert human analysts can be a bottleneck and make ecosystem or landscape-scale projects infeasible. This manuscript presents a method for rapidly analyzing the results of band-limited energy detectors, which are commonly used for the detection of passerine nocturnal flight calls, but which typically are beset by high false positive rates. We first manually classify a subset of the detected events as signals of interest or false detections. From that subset, we build a Random Forest model to eliminate most of the remaining events as false detections without further human inspection. The overall reduction in the labor required to separate signals of interest from false detections can be 80% or more. Additionally, we present an R package, flightcallr, containing functions which can be used to implement this new workflow. (C) 2013 Elsevier B.V. All rights reserved.
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