CLASSIFICATION OF BABY CRIES INTO DISTINCT CATEGORIES USING CONVOLUTIONAL NEURAL NETWORKS(CNN) WITH SOUND AND SPECTROGRAM ANALYSIS

Abstract

The act of a baby crying is a complex form of communication that reflects various physical, medical, and emotional states. Understanding the nuances within baby cries is essential, as it provides valuable insights into the baby’s needs and can assist in the early detection of developmental disorders and medical conditions. Machine Learning (ML) and Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), coupled with sound processing and data augmentation, play a pivotal role in this endeavor. This research explores methods encompassing data preprocessing, feature extraction, postprocessing, and classification. A primary focus is acoustic analysis and CNN for automatic feature extraction.