A comparison between automated and manual detection methods for detecting bird species richness in audio recordings
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Date
2018
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Publisher
University of New Brunswick
Abstract
Understanding long-term human impacts over large areas of the environment requires large-scale biological monitoring programs and efficient ways to analyze the volume of data that will be generated. Automatic recording devices (ARDs) can be deployed in the field to record sounds over long periods and are useful for monitoring vocal animal populations. The data can be analyzed by searching through portions of the audio species vocalizations (manual detection) or having software perform the search (automatic detection). Here we compare manual and automated techniques for detecting the presence of 43 bird species over 7 sites based on the vocalizations each method detected in field recordings. Results show that an automated method detected significantly more species than the manual method. Automated detection is, therefore, a viable option to efficiently analyze large audio datasets to determine bird species richness.