An Automatic Infants Cry Detection Using Linear Frequency Cepstrum Coefficients(LFCC)
Bhagatpatil Varsharani V, V. M. Sardar
Keywords: DBL, LFCC, Feature extraction, Euclidean distance, knn classifier, Matlab, VQ,LBG.
ABSTRACT: In this paper, we mainly focused on automation of Infant’s Cry. For this implementation we use LFCC for feature extraction and VQ codebook for matching samples using LBG algorithm. The baby crying samples collected from various crying baby having 0-6months age. There are 150 baby’s sound as training data, each of which represents the 30 hungry infant cries, 30 sleepy infant cries, 30 wanted to burp infant cries, 30 in pain infant cries, and 30 uncomfortable infant cries (could be because his/her diaper is wet/too hot/cold air or anything else).The testing data is 40, respectively 8 infant cries for each type of infant cry. The identification of infant cries based the minimum distance of Euclidean distance. The, classification of the cry in five classes neh – hunger owh – sleepy, heh – discomfort ,eair – lower gas, eh – burp.Here for classification of the cry our system is divided into two phases. First, in training phase, in which LFCC is applied for feature extraction, and then VQ codebooks are generated to compress the feature vectors. Second, is the testing phase in which features extraction and codebook generation of samples are repeated. Here, comparison of the codebook template of samples to the all the available templates in the database are carried based on Euclidian distance between them. LFCC effectively capture the lower as well as higher frequency characteristics than MFCC, hence we will get good results over MFCC.
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