Browsing by Author "Sanders, Sheri A."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Automatic Capture and Classification of Frog Calls
    (2020-08) Foran, Eliza; Underwood, Tenecious A.; Snapp-Childs, Winona; Sanders, Sheri A.
    Global frog populations are threatened by an increasing number of environmental threats such as habitat loss, disease, and pollution. Traditionally, in-person acoustic surveys of frogs have measured population loss and conservation outcomes among these visually cryptic species. However, these methods rely heavily on trained individuals and time-consuming field work. We propose an end-to-end workflow for the automatic recording, presence-absence identification, and web page visualization of frog calls by their species. The workflow encompasses recording of frog calls via custom Raspberry Pis, data-pushing to Jetstream cloud computer, and species classification by three different machine learning models: Random Forest, Convolutional Neural Network, and Recursive Neural Network.
  • Thumbnail Image
    Item
    Automatic recognition of frog calls
    (2019-07-28) Foran, Eliza G.; Suggs, Evan D.; Underwood, Tenecious A.; Snapp-Childs, Winona; Sanders, Sheri A.
    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.