Infant Crying Classification by Using Genetic Algorithm and Artificial Neural Network

Abstract

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.

Keywords

• Crying
• Mel-Frequency Cepstral Coefficients
• Linear Predictor Coefficients
• Neural Networks
• Genetic Algorithms