Whale optimization algorithm based classification of normal and abnormal cries in infants

Infants crying patterns are a means to communicate with the external world. Infants problems can be explored through their cry within first year. Significant changes in crying patterns give an idea about the neurological disorders, genetic problems and many more. The infant cry signal analysis is carried out to identify the cry causes like hunger, pain, discomfort, anxiety, etc. Different causes of infant crying are characterized by signals in the cry segments. In the proposed work a whale optimization algorithm is implemented to detect the emotion of the crying infant. The previous works include various algorithms for classification, however the novelty in this work can be attributed to processing only voiced part of the cry signal. Feature extraction techniques that include Melcepstral frequency coefficients (MFCC) are used to study and analyze the reason. Infant cry signal is analyzed for knowing the reason as well as for diagnosing pathological conditions because the signals contain more information about physical and physiological conditions. The database has been obtained from the neonatal ward of Harshita Hospital, Madurai. The algorithm has been tested on the test dataset consisting of 125 samples. The performance of the proposed method has been evaluated and the efficiency is found to be 94%as compared to 90% efficiency.