Paper Details

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

Sandhya S Jagtap, Premanand K Kadbe, “System Propose For Be Acquainted With newborn Cry Emotion Using Linear Frequency Cepstral Coefficient”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 3, Issue 2, pp. 150-156, February 2016.