Detection of asphyxia in infants using deep learning Convolutional Neural Network (CNN) trained on Mel Frequency Cepstrum Coefficient (MFCC) features extracted from cry sounds

Abstract

Deep Learning Neural Network (DLNN), is a new branch of machine learning with the ability for complex feature representatio Although it was mainly suited for image feature (since it was inspired by object recognition method of mammalian visual system), if any type of feature can be translate into image, other type of data could be fit for using DLNN. In this paper, we prove that Mel Frequency Cepstrum Coefficient (MFCC) feature generates from audio signal of infant cry could be used as input feature for the Convolution Neural Network (CNN)