Open AccessStudy of Linear Discriminant Analysis to Identify Baby Cry Based on DWT and MFCC
Author(s) -
Ledya Novamizanti,
Anggunmeka Luhur Prasasti,
Bangun Satria Utama
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/982/1/012009
Subject(s) - linear discriminant analysis , mel frequency cepstrum , crying , principal component analysis , infant crying , speech recognition , identification (biology) , pattern recognition (psychology) , feature extraction , artificial intelligence , discrete wavelet transform , feature (linguistics) , wavelet , computer science , psychology , wavelet transform , social psychology , linguistics , philosophy , botany , biology
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