Frog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog

Frogs play an important role in ecological systems, while frog species across the globe are threatened and declining. Therefore, it is valuable to estimate the frog population based on an intelligent computer system. Due to the success of deep learning (DL) in various pattern recognition tasks, prev...

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Autores principales:	Jie Xie, Mingying Zhu, Kai Hu, Jinglan Zhang, Harry Hines, Ya Guo
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Bioacoustic signal activity detection Multi-view spectrogram Lightweight CNN Loss function Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/67e7710c4b124390be626699700b55f3
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id	oai:doaj.org-article:67e7710c4b124390be626699700b55f3
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spelling	oai:doaj.org-article:67e7710c4b124390be626699700b55f32021-11-20T05:15:23ZFrog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog2666-827010.1016/j.mlwa.2021.100202https://doaj.org/article/67e7710c4b124390be626699700b55f32022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666827021001018https://doaj.org/toc/2666-8270Frogs play an important role in ecological systems, while frog species across the globe are threatened and declining. Therefore, it is valuable to estimate the frog population based on an intelligent computer system. Due to the success of deep learning (DL) in various pattern recognition tasks, previous studies have used DL-based methods for frog call analysis. However, the performance of DL-based systems is highly affected by their input (feature representation). In this study, we develop a frog calling activity detection system for continuous field recordings using a light convolutional neural network (CNN) with multi-view spectrograms. To be specific, a sliding window is first applied to continuous recordings for obtaining audio segments with a fixed duration. Then, the background noise is filtered out. Next, a multi-view spectrogram is used for characterizing those segments, which has more distinctive information than a single-view spectrogram. Finally, a lightweight CNN model is used for the detection of frog calling activity with a twin loss, where different train and test sets are used to validate the model’s robustness. Our experimental results indicate that the highest macro F1-score was 99.6 ± 0.2 and 96.4 ± 2.0 using 2016 and 2017 as the train data respectively, where CNN-GAP is used as the model with multi-view spectrogram as the input.Jie XieMingying ZhuKai HuJinglan ZhangHarry HinesYa GuoElsevierarticleBioacoustic signal activity detectionMulti-view spectrogramLightweight CNNLoss functionCyberneticsQ300-390Electronic computers. Computer scienceQA75.5-76.95ENMachine Learning with Applications, Vol 7, Iss , Pp 100202- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bioacoustic signal activity detection Multi-view spectrogram Lightweight CNN Loss function Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Bioacoustic signal activity detection Multi-view spectrogram Lightweight CNN Loss function Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95 Jie Xie Mingying Zhu Kai Hu Jinglan Zhang Harry Hines Ya Guo Frog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog
description	Frogs play an important role in ecological systems, while frog species across the globe are threatened and declining. Therefore, it is valuable to estimate the frog population based on an intelligent computer system. Due to the success of deep learning (DL) in various pattern recognition tasks, previous studies have used DL-based methods for frog call analysis. However, the performance of DL-based systems is highly affected by their input (feature representation). In this study, we develop a frog calling activity detection system for continuous field recordings using a light convolutional neural network (CNN) with multi-view spectrograms. To be specific, a sliding window is first applied to continuous recordings for obtaining audio segments with a fixed duration. Then, the background noise is filtered out. Next, a multi-view spectrogram is used for characterizing those segments, which has more distinctive information than a single-view spectrogram. Finally, a lightweight CNN model is used for the detection of frog calling activity with a twin loss, where different train and test sets are used to validate the model’s robustness. Our experimental results indicate that the highest macro F1-score was 99.6 ± 0.2 and 96.4 ± 2.0 using 2016 and 2017 as the train data respectively, where CNN-GAP is used as the model with multi-view spectrogram as the input.
format	article
author	Jie Xie Mingying Zhu Kai Hu Jinglan Zhang Harry Hines Ya Guo
author_facet	Jie Xie Mingying Zhu Kai Hu Jinglan Zhang Harry Hines Ya Guo
author_sort	Jie Xie
title	Frog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog
title_short	Frog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog
title_full	Frog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog
title_fullStr	Frog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog
title_full_unstemmed	Frog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog
title_sort	frog calling activity detection using lightweight cnn with multi-view spectrogram: a case study on kroombit tinker frog
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/67e7710c4b124390be626699700b55f3
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_version_	1718419486130831360

Frog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog

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