Acoustic Classification of Mosquitoes using Convolutional Neural Networks Combined with Activity Circadian Rhythm Information

Authors

Jaehoon Kim
Jeongkyu Oh
Tae-Young Heo

DOI

10.9781/ijimai.2021.08.009

Abstract

Many researchers have used sound sensors to record audio data from insects, and used these data as inputs of machine learning algorithms to classify insect species. In image classification, the convolutional neural network (CNN), a well-known deep learning algorithm, achieves better performance than any other machine learning algorithm. This performance is affected by the characteristics of the convolution filter (ConvFilter) learned inside the network. Furthermore, CNN performs well in sound classification. Unlike image classification,

Source Publication

International Journal of Interactive Multimedia and Artificial Intelligence

Recommended Citation

Kim, Jaehoon; Oh, Jeongkyu; and Heo, Tae-Young (2021) "Acoustic Classification of Mosquitoes using Convolutional Neural Networks Combined with Activity Circadian Rhythm Information," International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 7: Iss. 2, Article 16.
DOI: 10.9781/ijimai.2021.08.009
Available at: https://ijimai.researchcommons.org/ijimai/vol7/iss2/16