Implementation of Artificial Intelligence for Classification of Frogs in Bioacoustics

Abstract

This research presents the implementation of artificial intelligence (AI) for classification of frogs in symmetry of the bioacoustics spectral by using the feedforward neural network approach (FNNA) and support vector machine (SVM). Recently, the symmetry concept has been applied in physics, and in mathematics to help make mathematical models tractable to achieve the best learning performance. Owing to the symmetry of the bioacoustics spectral, feature extraction can be achieved by integrating the techniques of Mel-scale frequency cepstral coefficient (MFCC) and mentioned machine learning algorithms, such as SVM, neural network, and so on. At the beginning, the raw data information for our experiment is taken from a website which collects many kinds of frog sounds. This in fact saves us collecting the raw data by using a digital signal processing technique. The generally proposed system detects bioacoustic features by using the microphone sensor to record the sounds of different frogs. The data acquisition system uses an embedded controller and a dynamic signal module for making high-accuracy measurements. With regard to bioacoustic features, they are filtered through the MFCC algorithm. As the filtering process is finished, all values from ceptrum signals are collected to form the datasets. For classification and identification of frogs, we adopt the multi-layer FNNA algorithm in machine learning and the results are compared with those obtained by the SVM method at the same time. Additionally, two optimizer functions in neural network include: scaled conjugate gradient (SCG) and gradient descent adaptive learning rate (GDA). Both optimization methods are used to evaluate the classification results from the feature datasets in model training. Also, calculation results from the general central processing unit (CPU) and Nvidia graphics processing unit (GPU) processors are evaluated and discussed. The effectiveness of the experimental system on the filtered feature datasets is classified by using the FNNA and the SVM scheme. The expected experimental results of the identification with respect to different symmetry bioacoustic features of fifteen frogs are obtained and finally distinguished.

Thermal Conductivity Modeling of Nanofluids Contain MgO Particles by Employing Different Approaches

“…ANNs are applicable in different fields of study for predicting the behavior of the systems and their modeling [47][48][49][50][51]. The third approach used for forecasting the TC of the nanofluids with MgO particles is GMDH.…”

Exploiting deep neural network and long short-term memory method-ologies in bioacoustic classification of LPC-based features

“…This study is inspired from the feature classification experiments in [ 16 ]. The methods in [ 16 ] are to use the MFCC digital filtering algorithm to extract features from the original acoustic signals every single specy of the amphibian.…”

“…This study is inspired from the feature classification experiments in [ 16 ]. The methods in [ 16 ] are to use the MFCC digital filtering algorithm to extract features from the original acoustic signals every single specy of the amphibian. The methods in [ 16 ] adjust the pre-emphasis coefficients to create multiple filtering effects, collect the feature spectral values, and construct the training datasets.…”

“…Technically speaking, bioacoustic features and classification, after collecting and processing data, produce meaningful feature information and provide a better method to measure ecosystem changes [ 15 ]. A research project conducted at the Academia Sinica Biodiversity Research Center [ 16 ] has collected and analyzed audio field signals in forests, thereby constructing characteristic sound field training datasets models for forest environments. Different from [ 16 ], this presented algorithms used in this study is entirely new approaches of more samples.…”

“…A research project conducted at the Academia Sinica Biodiversity Research Center [ 16 ] has collected and analyzed audio field signals in forests, thereby constructing characteristic sound field training datasets models for forest environments. Different from [ 16 ], this presented algorithms used in this study is entirely new approaches of more samples.…”