Infant cry recognition using excitation source features
In this work, source features are explored for classifying infant cries. Different types of infant cries considered in this work are hunger, pain and wet-diaper. The various excitation source features explored in this work are source features namely epoch interval contour (EIC), epoch strength contour (ESC), epoch sharpness, slope of EIC and ESC features. In this work Gaussian Mixture Models (GMM) are used for classifying the different types of infant cries by utilizing the proposed features. Infant cry database collected under telemedicine project at IIT-KGP has been used for carrying out this study. The recognition performance using combination of evidences is found to be superior over individual systems.