System Propose For Be Acquainted With newborn Cry Emotion Using Linear Frequency Cepstral Coefficient

Abstract

In this paper, we mainly paying attention on mechanization of Infant’s Cry. For this implementation we use
LFCC for feature extraction and VQ codebook for toning samples using LBG algorithm. The newborn crying samples
composed from various crying baby having 0-6months age. There are 27 babie’s sound as training data, each of which
represents the 7 hungry infant cries, 4 sleepy infant cries, 10 in pain infant cries, and 6 uncomfortable infant cries. The
testing data is one of the traning newborn crying sample. The discovery of infant cries based the least amount distance of
Euclidean distance. The, classification of the cry in four classes neh for hunger owh for sleepy, heh for discomfort ,eair
for lower gas.
Here for classification of the cry our system is alienated into two phases. First is training phase, in which LFCC is used
for feature extraction, and then VQ codebooks are engender to compress the feature vectors. Second is the testing stage
in which features extraction and codebook production of samples are recurring. Here, estimation of the codebook
blueprint of samples to the all the existing patterns in the database are carried based on Euclidian distance between
them. LFCC efficiently take into safekeeping the lower as well as higher frequency characteristics than MFCC, hence we
will get high-quality results over MFCC.