Diagnostic feasibility of time domain features for detecting and characterizing cry cause factors - an investigation

Abstract

ABSTRACT The very first cry of an infant gives vital information about the health of infant, and as they grow the acoustics change with the development of their vocal tract system. This reflects the learning mechanism of infant cry-cause factors, which upon solving will give a huge impact in the areas of medical and household. The behaviour of infant cry records is frequently used for non-invasive infant health inspection and monitoring. Automated approaches for forecasting health status, on the other hand, are highly dependent on the features extracted. In this paper, the diagnostic feasibility of the time domain features to detect and discriminate various cry-cause factors of cry signals is investigated. Mean, peak value, RMS, crest factor, Impulse factor, shape factor, energy, and clearance factor are the features employed in this work. It is discovered that, among the features investigated, RMS is more effective than all other features in detecting cry-cause factors with a Probability value (P) of 2.23307 × 10−6 and it offers an accuracy of 91.67%, sensitivity of 90%, and specificity of 93.33%.