Study of Linear Discriminant Analysis to Identify Baby Cry Based on DWT and MFCC

Abstract

Abstract Baby crying is a common behavior among babies and is a means of verbal communication for babies who express their needs and desires. A baby’s cry identification system is needed because it makes it easy for adults to find out the meaning of a baby’s cry. This study proposes a system for classifying infant crying sounds using Linear Discriminant Analysis (LDA) with Discrete Wavelet Transform (DWT) and Mel-frequency Cepstral Coefficient (MFCC) as a feature extraction method. Based on experiments, the system can identify the sound of crying babies grouped into 5 (five) classes, namely discomfort, hunger, colds, burp, and drowsiness. The system achieves an accuracy of 94% and an average computing time of 1.5506 seconds. The performance of Linear Discriminant Analysis (LDA) outperformed Principal Component Analysis (LDA) in the identification of crying babies