Automatic recognition of frog calls. Foran, Eliza G.; Suggs, Evan D.; Underwood, Tenecious A.; Snapp-Childs, Winona; Sanders, S., A. 2019.
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Paper Website doi abstract bibtex Recording animal calls and vocalizations is a time-honored data collection method in various fields of biological and environmental science. In the past, the only method available for analyzing such recordings involved extensive training of human experts. Now, however, machine learning techniques have made automatic recognition of such vocalizations possible. Automatic recognition of animal calls and vocalizations is desirable on two fronts: it reduces the burden of (at least initial) data analysis, and supports non-intrusive environmental monitoring. Here, we outline a proof-of-concept workflow that will make the quest from collecting data to understanding data more attainable for researchers. We simulate this data collection process by collecting animal (frog) calls using recording devices and Raspberry Pi's, then feed this data to a database and virtual machine hosted on XSEDE resources (i.e. Jetstream and Wrangler). We then show how database pulling, machine learning, and visualization works on Jetstream.
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abstract = {Recording animal calls and vocalizations is a time-honored data collection method in various fields of biological and environmental science. In the past, the only method available for analyzing such recordings involved extensive training of human experts. Now, however, machine learning techniques have made automatic recognition of such vocalizations possible. Automatic recognition of animal calls and vocalizations is desirable on two fronts: it reduces the burden of (at least initial) data analysis, and supports non-intrusive environmental monitoring. Here, we outline a proof-of-concept workflow that will make the quest from collecting data to understanding data more attainable for researchers. We simulate this data collection process by collecting animal (frog) calls using recording devices and Raspberry Pi's, then feed this data to a database and virtual machine hosted on XSEDE resources (i.e. Jetstream and Wrangler). We then show how database pulling, machine learning, and visualization works on Jetstream.},
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author = {Foran, Eliza G.; Suggs, Evan D.; Underwood, Tenecious A.; Snapp-Childs, Winona; Sanders, Sheri A.},
doi = {10.5967/PS4S-D421}
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