Cry-Based Infant Pathology Classification Using GMMs

H. F. Alaie, L. Abou-Abbas, C. Tadj

Traditional studies of infant’s cry signal focus more on non-pathological-based classification of infants. In this article, we introduce an inexpensive health care system which carries out acoustic analysis of the unclean noisy infants’ cry signals to extract and measure certain cry characteristics quantitatively and classifies healthy and sick newborn infants only according to their cries. In the task of this newborn cry-based diagnostic system, the dynamic MFCC feature along with static Mel-Frequency Cepstal Coefficients (MFCCs) are selected and extracted for both expiratory and inspiratory cry vocalizations in order to make the most discriminative and informative feature vector. And then, we create a unique cry-pattern for each cry vocalization type and pathological condition by introducing a novel idea using Boosting Mixture Learning (BML) method to derive either healthy or pathological subclass models separately from the Gaussian Mixture Model-Universal Background Model (GMM-UBM). Our newborn cry-based diagnostic system (NCDS) has a hierarchical scheme which is a treelike combination of individual classifiers. Moreover, a score level fusion of the proposed expiratory and inspiratory cry-based subsystems is performed to make more reliable decision. The experimental results indicate that the adapted BML method has lower error rates than Bayesian or maximum a posteriori probability (MAP) adaptation as a reference method.

Keywords: Gaussian mixture models, Universal background model, on Mel Frequency Cepstral Coefficients, likelihood ratio scores, newborn infant cries, expiratory and inspiratory sounds