Show simple item record

dc.contributor.author Foran, Eliza
dc.contributor.author Underwood, Tenecious A.
dc.contributor.author Snapp-Childs, Winona
dc.contributor.author Sanders, Sheri A.
dc.date.accessioned 2020-08-11T12:52:49Z
dc.date.available 2020-08-11T12:52:49Z
dc.date.issued 2020-08
dc.identifier.uri http://hdl.handle.net/2022/25760
dc.description.abstract 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. en
dc.language.iso en en
dc.rights.uri https://creativecommons.org/licenses/by/4.0/ en
dc.title Automatic Capture and Classification of Frog Calls en
dc.type Presentation en


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search IUScholarWorks


Advanced Search

Browse

My Account

Statistics