# Cicada Species Recognition Based on Acoustic Signals
## 2 Related Works
### Conventional Methods  
Before deep learning methods became increasingly common, researchers focused on using conventional methods, such as support vector machine (SVM) and random forests (RF), for insect or animal detection and classification. The performances of conventional methods are dependent on the feature extraction process to obtain high performance in terms of classification accuracy [6]. In 2007, [PERSON], [PERSON], and [PERSON] [7] attempted to classify species of crickets and cicadas using their acoustic signals with a probabilistic neural network (PNN) and Gaussian mixture models (GMM). The dataset consists of audio recordings of 220 species of crickets and cicadas obtained from the Singing Insects of North America (SINA) collection. The recognition process has two main stages, which are signal parameterization and classification. Figure 1 shows the detailed steps of the acoustic insect recognition process. This experiment obtained a high accuracy of 99% and above for classifications at levels of singing insect and family and 90% for classification at species level using the fusion of PNN and GMM. However, the identification accuracy of this fusion was found to be insufficient for practical applications despite the performance gain. In 2010, [PERSON] and [PERSON] [8] conducted research to identify different sounds of insects automatically with the aim of providing convenience for technicians in pest management. A total of 50 different insect sounds were collected from the insect sound library from the Agricultural Research Service (ARS) of the United States Department of Agriculture (USDA). This experiment classifies these insect sounds according to their sound types, such as \"movement and feeding sounds of soil invertebrates\", \"defensive stridulation of soil insects\", \"wind and abdominal vibration sounds\", etc. To extract the features of the insect sounds, sub-band-based cepstral (SBC) was used, while a Hidden Markov Model (HMM) was used for classification. The experiment achieved a high classification accuracy of 90.98%. As the dataset used in this research was selected from noise-free sections of the recorded signal, the model may not work well with insect sounds with a noisy background. Figure 1: Diagram of the acoustic insect recognition process for \(N\) classes [5]. In 2019, research on the automated classification of bee species and hornet species using acoustic analysis was conducted by [PERSON] and [PERSON] [9]. Through this research, they aimed to monitor predator-prey relationships that can contribute to the ecological domain. The species chosen for the experiment were three honey-bee species and a hornet. The datasets used were self-recorded in rural areas and remote forests in Japan. To extract the feature values of the signals, MFCC was used, while SVM was used for classification. The experiment succeeded with high recall and precision of 0.96 to 1.00 in distinguishing bees and hornets from the environment and relatively high recalls and precision of 0.7 to 1.0 in identifying bees and hornets at the species level. Despite the high performances obtained, the model faces difficulties in classifying species with subtle differences in their sound features. Furthermore, in the same year, [PERSON] et al. [1] conducted experiments to identify 343 species of crickets, katydids, and cicadas by analyzing their acoustic signals. Out of the 343 species dataset, sound recordings of 255 species of crickets, katydids, and cicadas were obtained from the SINA collection, while sound recordings of the remaining 88 species of cicadas were retrieved from the Insect Singers corpora. In this research, they have proposed a new sound parameterization technique, a fusion between MFCC and LFCC, to represent the sounds of insects more effectively. As for the classification method, SVM and RF algorithms were used. Experiments were carried out using different combinations of feature extraction methods, MFCC, LFCC, and MFCC/LFCC fusion, and classifiers, SVM and RF, to obtain the best result.