Gomes Selman, Jonathan and Demir, Nikita (2019) Automatic Detection for Acoustic Monitering of Wild Animals. Technical Report. Stanford InfoLab. (Publication Note: Final report for Stanford's CS231n image analysis course.)
|PDF (Final report for Stanford's CS231n image analysis class.)|
We achieve, to our knowledge, state of the art results for detecting elephant calls from acoustic data on two different tasks of interest to researchers: 1) A segmentation task where we predict for each time step input whether there is an elephant call present and 2) A trigger word detection task where we predict the completion of an elephant call. The latter is of particular interest because of its potential to allow researchers to run in real time computationally efficient models on low-cost devices. We used data collected by passive acoustic monitoring (PAM) systems in the African jungle provided generously by the Cornell Lab of Ornithology.
|Item Type:||Techreport (Technical Report)|
|Deposited By:||Andreas Paepcke|
|Deposited On:||04 Sep 2019 09:12|
|Last Modified:||04 Sep 2019 09:12|
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