Premature Infant Cry Classification via Elephant Herding Optimized Convolutional Gated Recurrent Neural Network

Abstract

Premature babies scream to make contact with their mothers or other people. Infants communicate via their screams in different ways based on the motivation behind their cries. A considerable amount of work and focus is required these days to preprocess, extract features, and classify audio signals. This research aims to propose a novel Elephant Herding Optimized Deep Convolutional Gated Recurrent Neural Network (EHO-DCGR net) for classifying cry signals from premature babies. Cry signals are first preprocessed to remove distortion caused by short sample times. MFCC (Mel-frequency cepstral coefficient), Power Normalized Cepstral Coefficients (PNCC), BFCC (Bark-frequency cepstral coefficient), and LPCC (Linear Prediction cepstral coefficient) are used to identify abnormal weeping through their prosodic aspects. The Elephant Herding optimization (EHO) algorithm is utilized for choosing the best features from the extracted set to form a fused feature matrix. These characteristics are then used to categorize premature baby cry sounds using the DCGR net. The proposed EHO-DCGR net effectiveness is measured by precision, specificity, recall, and F1-score, accuracy. According to experimental fallouts, the proposed EHO-DCGR net detects baby cry signals with an astounding 98.45% classification accuracy. From the experimental analysis, the EHO-DCGR Net increases the overall accuracy by 12.64%, 3.18%, 9.71% and 3.50% better than MFCC-SVM, DFFNN, SVM-RBF and SGDM respectively.