Cicada Species Recognition Based on Acoustic Signals

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%.

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

“…New areas will open up where DAS technology can be applied. In addition to studies already carried out using a distributed acoustic sensor on the behavior of the red weevil in palm trees [74], acoustic observations of cicadas and bees seem to be the most obvious future work [75,76]. Perhaps this will provide key data to address issues related to declining bee populations around the world.…”

A convolutional neural network with image and numerical data to improve farming of edible crickets as a source of food—A decision support system

“…The selected machine learning algorithm (s) dictates the type of features extracted. For instance, literature shows that shallow machine learning algorithms were trained on numerical features (Zhang and Guo, 2010 ; Yazgaç et al, 2016 ; Kawakita and Ichikawa, 2019 ; Noda et al, 2019 ), while deep learning algorithms were trained on numerical or spectrogram image features (Dong et al, 2018 ; Kiskin et al, 2020 ; Arpitha et al, 2021 ; Tey et al, 2022 ). Herein, we extracted numerical (chroma, MFCC, and Linear Frequency Cepstral Coefficients (LFCC)) and image (i.e., spectrograms) features since they were widely used by other researchers (Noda et al, 2016 , 2019 ; Yazgaç et al, 2016 ; Zamanian and Pourghassem, 2017 ; Dong et al, 2018 ; Kawakita and Ichikawa, 2019 ; Tey et al, 2022 ).…”

“…Zamanian and Pourghassem ( 2017 ) used multi-layered perceptron (MLP) and genetic algorithms to classify cicada species based on their sounds. Dong et al ( 2018 ) employed convolutional neural networks (CNN) with enhanced spectrograms for insect recognition, while Tey et al ( 2022 ) used spectrogram images and deep learning algorithms for cicada species recognition. These approaches achieved accuracy rates ranging from 77.78% to 99.13%.…”

Acoustic monitoring for tropical insect conservation

“…Much of the focus of automated recognition has been on vertebrate taxa, especially birds and mammals. Recently however, automatic recognition algorithms have been developed to discriminate different mosquito and bee species from their flight sounds (Kawakita and Ichikawa 2019), and cicada, cricket and katydid species based on their calls (Noda et al 2019, Tey et al 2022, Faiß and Stowell 2023), some with impressive accuracies of species-level discrimination (90-98%). Most of the algorithms have however been developed using recordings of individual species available in databases, with data augmentation techniques to introduce noise.…”