Comparison of Supervised-learning Models for Infant Cry Classification / Vergleich von Klassifikationsmodellen zur Säuglingsschreianalyse

Abstract

Abstract Cries of infants can be seen as an indicator for several developmental diseases. Different types of classification algorithms have been used in the past to classify infant cries of healthy infants and those with developmental diseases. To determine the ability of classification models to discriminate between healthy infant cries and various cries of infants suffering from several diseases, a literature search for infant cry classification models was performed; 9 classification models were identified that have been used for infant cry classification in the past. These classification models, as well as 3 new approaches were applied to a reference dataset containing cries of healthy infants and cries of infants suffering from laryngomalacia, cleft lip and palate, hearing impairment, asphyxia and brain damage. Classification models were evaluated according to a rating schema, considering the aspects accuracy, degree of overfitting and conformability. Results indicate that many models have issues with accuracy and conformability. However, some of the models, like C5.0 decision trees and J48 classification trees provide promising results in infant cry classification for diagnostic purpose.