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 representation compared to traditional 4th-generation neural networks. 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). The result shows CNN can be used to classify between normal and pathological (asphyxiated) cry with 94.3% accuracy in training set and 92.8% accuracy in testing set.