Classification of asphyxia infant cry using hybrid speech features and deep learning models

摘要

Single speech feature such as Mel-Frequency Cepstral Coefficient (MFCC) has been used in most of the studies to classify asphyxia cry among infants. Other speech features such as Chromagram, Mel-scaled Spectrogram, Spectral Contrast and Tonnetz have not been reported in any study related to the classification of asphyxia cry. The study investigated the use of hybrid features of MFCC, Chromagram, Mel-scaled Spectrogram, Spectral Contrast and Tonnetz and deep learning models in classifying asphyxia cry. Deep learning models such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN) were used to classify infant cry between normal/non-asphyxia and asphyxia. The performance of the deep learning models was compared using concatenated hybrid features and single feature of MFCC. The Baby Chillanto Database was used in this study. CNN model performed better than DNN models when MFCC was used. DNN models performed better with hybrid features compared to that with single feature of MFCC. DNN with multiple hidden layers achieved an accuracy of 100% in classifying normal and asphyxia cry, and 99.96% for non-asphyxia and asphyxia cry when the hybrid features were used.