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Acta Medica Iranica، جلد ۵۸، شماره ۱۰، صفحات ۵۳۱-۵۳۹
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عنوان انگلیسی Infant Crying Classification by Using Genetic Algorithm and Artificial Neural Network
چکیده انگلیسی مقاله Cry as the only way of communication of babies with the surrounding environment can be happened for many reasons such as diseases, suffocation, hunger, cold and heat feeling, pain and etc. So, the analyzing and detection of its source is very important for parents and health care providers. So the present study designed with the aim to test the performance of neural networks in the identification of the source of babies crying. Present study combines the genetic algorithm and artificial neural network with (Linear Predictive Coding) LPC and MFCC (Mel-Frequency Cepstral Coefficients) to classify the babies crying. The results of this study indicate the superiority of the proposed method compared to the other previous methods. This method could achieve the highest accuracy in the classification of newborns crying among the previous studies. Developing methods for classification audio signal analysis are promising and can be effectively applied in different areas such as babies crying.
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نویسندگان مقاله | Azadeh Bashiri
Department of Health Information Management, School of Management and Medical Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran. AND Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran


| Roghaye Hosseinkhani
Department of Electrical Engineering, Islamic Azad University, Kerman Branch, Kerman, Iran
نشانی اینترنتی https://acta.tums.ac.ir/index.php/acta/article/view/8282
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