Premature Infant Cry Classification via Deep Convolutional Recurrent Neural Network Based on Multi-class Features

The cry of a premature infant is an attempt to connect with its mother or others. The newborns are communicated in different ways depending on the reason for their screams. In recent days, the preprocessing, feature extraction, and classification of audio signals require expert attention and a lot of effort. In this paper, a novel deep convolutional recurrent neural network (DCR net) has been proposed to classify the premature infant cry signal into different categories. The acquisition of the cry signal generally requires a lengthy observation period and several activity processes to obtain all the signals of the premature infant. The relevant multi-class frequency features are extracted by using the MFCC (Mel-frequency cepstral coefficient), BFCC (Bark-frequency cepstral coefficient), and LPCC (linear prediction cepstral coefficient) features, which are combined to create a fused feature matrix that is helpful in the classification of pathological crying. Based on these features, the DCR net is used to classify sounds in the premature infant cry. The sound of the target cry signal is classified into five categories: “neh” means hunger, “heh” means discomfort, “eh” means burping, “eair” means cramps, and “owh” means fatigue. The efficiency of the DCR net was estimated with some metrics such as specificity, precision, accuracy, recall, and F1 score. The experimental fallouts disclose that the proposed DCR net attains a better classification accuracy of 97.27% for identifying infant cry signals. The DCR net increases the overall performance range by 8.61%, 11.58%, 0.54%, and 17.03% better than SVM-RBF, MFCC-SVM, optimized deep learning model, and hidden Markov model, respectively.