Computer automated frog vocalization results from Picayune Strand State Forest, Florida 2011-2012
June 6, 2017
This dataset includes the results of a computer automated anuran vocalization recognition analysis of audio recordings made at Picayune Strand State Forest in 2011 and 2012. The audio files were scanned for 11 species of frogs and toads using the commercially available software program Song Scope (ver. 4.1.3). All detections by the software are listed here along with a "result" field which indicates whether or not the computer detection was a true positive or a false positive.
Citation Information
Publication Year | 2017 |
---|---|
Title | Computer automated frog vocalization results from Picayune Strand State Forest, Florida 2011-2012 |
DOI | 10.5066/F7MP51H4 |
Authors | Hardin Waddle, Jeromi M. Hefner, Susan Walls |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Wetland and Aquatic Research Center - Gainesville, FL |
Rights | This work is marked with CC0 1.0 Universal |
Related Content
A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing
The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different typ
Authors
Thierry A. Chambert, J. Hardin Waddle, David A.W. Miller, Susan C. Walls, James D. Nichols
Related Content
A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing
The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different typ
Authors
Thierry A. Chambert, J. Hardin Waddle, David A.W. Miller, Susan C. Walls, James D. Nichols