Deep Learning Locally Trained Wildlife Sensing in Real Acoustic Wetland Environment
Name
Duhart-Tidzam-SIRS-2018.pdf
Description
Accepted version
Size
2.8 MB
Format
Adobe PDF
Checksum (MD5)
f4d81277c9c47fc77b7fe8e4a7db8371
Author(s)
Duhart, Clement
Dublon, Gershon
Mayton, Brian Dean
Paradiso, Joseph A
Date Issued
2019
Journal
Communications in Computer and Information Science
Publisher
Springer Singapore
Citation
Duhart, Clement, Dublon, Gershon, Mayton, Brian Dean and Paradiso, Joseph A. "Deep Learning Locally Trained Wildlife Sensing in Real Acoustic Wetland Environment." Communications in Computer and Information Science, 968.
Version
Author's final manuscript
Abstract
© 2019, Springer Nature Singapore Pte Ltd. We describe ‘Tidzam’, an application of deep learning that leverages a dense, multimodal sensor network installed at a large wetland restoration performed at Tidmarsh, a 600-acre former industrial-scale cranberry farm in Southern Massachusetts. Wildlife acoustic monitoring is a crucial metric during post-restoration evaluation of the processes, as well as a challenge in such a noisy outdoor environment. This article presents the entire Tidzam system, which has been designed in order to identify in real-time the ambient sounds of weather conditions as well as sonic events such as insects, small animals and local bird species from microphones deployed on the site. This experiment provides insight on the usage of deep learning technology in a real deployment. The originality of this work concerns the system’s ability to construct its own database from local audio sampling under the supervision of human visitors and bird experts.
MIT Department
Massachusetts Institute of Technology. Responsive Environments Group
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-981-13-5758-9_1
