Construction and validation of an automated time label segmentation method for infant cries

Infant cries serve as critical indicators of an infant's physiological and psychological states, holding essential clinical value in distinguishing between normal and pathological conditions during early development. However, most studies on infant cries failed to segment between inhalation and exhalation during the crying analysis because segmentation remains great challenge. Therefore, we developed an audio data analysis tool capable of accurately segmenting inhalation and exhalation phases in infant cries, followed by extraction of the duration, frequency, and intensity parameters of each segment. Here, 226 sound clips of infants' cries were collected and analyzed. The results revealed the significant differences in sound parameters between premature and term infants. The whole process allows automatic identification and digital collection of infant cries, and it provides an objective method to evaluate the significance behind the cries.