Citation

Tey, Wan Teng and Tee, Connie and Choo, Kan Yeep and Goh, Michael Kah Ong (2022) Cicada Species Recognition Based on Acoustic Signals. Algorithms, 15 (10). p. 358. ISSN 1999-4893

Text algorithms-15-00358-v3.pdf - Published Version Restricted to Repository staff only Download (4MB)

Abstract

Traditional methods used to identify and monitor insect species are time-consuming, costly, and fully dependent on the observer’s ability. This paper presents a deep learning-based cicada species recognition system using acoustic signals to classify the cicada species. The sound recordings of cicada species were collected from different online sources and pre-processed using denoising algorithms. An improved Härmä syllable segmentation method is introduced to segment the audio signals into syllables since the syllables play a key role in identifying the cicada species. After that, a visual representation of the audio signal was obtained using a spectrogram, which was fed to a convolutional neural network (CNN) to perform classification. The experimental results validated the robustness of the proposed method by achieving accuracies ranging from 66.67% to 100%.

Item Type:	Article
Uncontrolled Keywords:	Cicada species recognition, acoustic signal, deep learning, spectrogram, Härmä syllable segmentation
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Engineering (FOE) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	15 Dec 2022 07:14
Last Modified:	15 Dec 2022 07:14
URII:	http://shdl.mmu.edu.my/id/eprint/10813

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