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Implementation of Artificial Intelligence for Classification of Frogs in Bioacoustics

1
Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan
2
Smart Machinery and Intelligent Manufacturing Research Center, National Formosa University, Yunlin 632, Taiwan
3
Department of Information Management, National Formosa University, Yunlin 632, Taiwan
4
Fudan Senior High School, Taoyuan 324, Taiwan
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(12), 1454; https://doi.org/10.3390/sym11121454
Received: 9 November 2019 / Revised: 22 November 2019 / Accepted: 24 November 2019 / Published: 26 November 2019
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.
Keywords: artificial intelligence (AI); feedforward neural network approach (FNNA); symmetry; Mel-scale frequency cepstral coefficient (MFCC); machine learning (ML); graphics processing unit (GPU); support vector machine (SVM); bioacoustics artificial intelligence (AI); feedforward neural network approach (FNNA); symmetry; Mel-scale frequency cepstral coefficient (MFCC); machine learning (ML); graphics processing unit (GPU); support vector machine (SVM); bioacoustics
MDPI and ACS Style

Chao, K.-W.; Hu, N.-Z.; Chao, Y.-C.; Su, C.-K.; Chiu, W.-H. Implementation of Artificial Intelligence for Classification of Frogs in Bioacoustics. Symmetry 2019, 11, 1454.

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