A Deep Learning-Based Coyote Detection System Using Audio Data

The attacks on livestock, human, and crops by coyotes are occurring over the United States, while traditional simple management such as public education about the method of avoiding coyotes and coyote hunting contests to reduce their numbers are executed. There are not sufficient cases of technical...

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Bibliographic Details
Published in:	2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) pp. 170 - 175
Main Authors:	Jung, Heesun, Kwon, Bokyung, Kim, Youngbin, Lee, Yejin, Park, Jihyeon, Pegg, Griffin, Wang, Yaqin Mia, Smith, Anthony H.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 20-02-2023
Subjects:	Analytical models audio classification Crops Data models Education Feature extraction image classification Learning (artificial intelligence) Machine learning machito learning spectrogram features
Online Access:	Get full text
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Summary:	The attacks on livestock, human, and crops by coyotes are occurring over the United States, while traditional simple management such as public education about the method of avoiding coyotes and coyote hunting contests to reduce their numbers are executed. There are not sufficient cases of technical approaches or research about the preventing coyotes. The method of a coyote howling sound classification using machine learning and feature extraction to reduce the damage from the coyotes is needed. This paper applies the optimal model created by comparing and analyzing various combinations of machine learning and feature extraction methods to the coyote detection platform. It is expected that an additional technical approach to current coyote damage prevention can improve the accuracy and make the previous management more practical.
ISSN:	2831-6983
DOI:	10.1109/ICAIIC57133.2023.10067023