# Cicada Species Recognition Based on Acoustic Signals
## 2 Related Works
### Deep Learning Methods  
Following the advancement and success of deep learning models in image processing, researchers have started to explore more possibilities of this powerful architecture by using it for acoustic signal recognition. The basic idea of this process is to visualize the acoustic signals or extracting the features of the signals, similar to conventional methods, and pass it to the model for classification. The use of deep learning methods has also become increasingly popular in insect detection and classification as more studies are conducted on insect acoustic analysis using deep learning models, such as multilayer perceptron (MLP) and convolutional neural network (CNN). As early as 2004, [PERSON] [11] developed a model to identify four species of grasshopper using their bioacoustics signals in a noisy environment. The dataset was self-recorded by the researcher at various sites and habitats of the species in England. Time Domain Signal Coding (TDSC) and MLP were used in this experiment. A total of 13 sound sources were used to train the model, which includes sounds of four grasshopper species, birds, vehicles, and more. The experiments achieved an overall high accuracy of 81.8% to 100% in classifying four grasshopper species from the 13 sound sources when a threshold of 0.9 was applied to remove classification results below the threshold. Additionally, this model was also able to identify the sounds of a grasshopper species, a light aircraft, and a bird alarm call from a sound recording, which shows its potential in general sound mapping applications. In 2017, [PERSON] and [PERSON] [12] proposed to use an MLP classifier to classify five cicada species based on the spectral and temporal features of their sound. The dataset used in this experiment was self-recorded by the researchers in the natural environment in Iran, and only the calling songs of the cicada species were selected. Levenberg Marquardt (LM) was chosen as the training algorithm. At the end of the experiment, high accuracy of 96.70% was achieved when all features were weighted. The model was further improved by applying a genetic algorithm for feature selection and obtained a higher accuracy rate of 99.13%. Given the outstanding classification rate, the researchers are confident that the model proposed not only can be used to classify cicada species but is applicable for the classification of other insect species as well. A downside of this study is that the scope is limited to classifying cicada species based on their calling songs only, yet cicada species produce other sounds to communicate, including courtship and distress sounds. Moreover, in 2018, [PERSON], [PERSON], and [PERSON] [13] introduced an approach for insect recognition using enhanced spectrograms and CNN. The dataset used consists of sound recordings of 47 insect species retrieved from the sound library of ARS of USDA. The spectrogram images were generated in MATLAB and enhanced using contrast-limit adaptive histogram equalization (CLAHE) to reduce noise. The resulting enhanced spectrograms were two-dimensional grey images, and the images were directly passed to the CNN model for classification. The experiment succeeded with a high average accuracy rate of 97.8723%, and it is said to be the highest accuracy obtained among past studies conducted on the same dataset. However, since the model has not been used upon large scale datasets, it remains unknown whether the performance of the model persists if tested. In early 2021, [PERSON], [PERSON], and [PERSON] [14] proposed a model to classify four mosquito species based on the sound of their wingbeats. The dataset used was obtained from the Kaggle repository, which consists of 1200 samples of mosquito wingbeat sounds. The signals were pre-processed by framing using Hamming window function and feature scaling using an octave scale. This experiment proposed to develop a CNN model with unsupervised transfer learning strategies.