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TEAM

Chirp Checker

Andrew Merwin, Caleb Fong, B Mede, Yang Yang, Robert Cass, Calvin Yost-Wolff

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The nocturnal soundscapes of late summer and autumn are replete with the familiar chirps, trills, and buzzes of singing insects. But these cryptic performers often remain anonymous and underappreciated.

The goal of this project is to build a machine learning model to identify the presence of insects in sound files and to coarsely categorize the sounds as crickets, katydids, or cicadas. This model could be applied to filter large volumes of passively recorded audio from ecological studies of insects and could serve as a first step towards more sophisticated models and apps that identify insect songs to the species level.

Importantly, I think this project may be feasible, even with our time constraints.

There are a few resources available for acquiring labeled audio files:
(1) Singing Insects of North America | https://orthsoc.org/sina/
(2) xeno-canto | https://xeno-canto.org/
(3) Macaulay Library of Natural Sounds at Cornell | https://www.macaulaylibrary.org/

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