文章基本信息

标题：INFANT CRIES IDENTIFICATION BY USING CODEBOOK AS FEATURE MATCHING, AND MFCC AS FEATURE EXTRACTION
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作者：MEDHANITA DEWI RENANTI ; AGUS BUONO ; WISNU ANANTA KUSUMA 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2013
卷号：56
期号：3
出版社：Journal of Theoretical and Applied
摘要：In this paper, we focused on automation of Dunstan Baby Language. This system uses MFCC as feature extraction and codebook as feature matching. The codebook of clusters is made from the proceeds of all the baby�s cries data, by using the k-means clustering. The data is taken from Dunstan Baby Language videos that has been processed. The data is divided into two, training data and testing data. There are 140 training data, each of which represents the 28 hungry infant cries, 28 sleepy infant cries, 28 wanted to burp infant cries, 28 in pain infant cries, and 28 uncomfortable infant cries (could be because his diaper is wet/too hot/cold air or anything else). The testing data is 35, respectively 7 infant cries for each type of infant cry. The research varying frame length: 25 ms/frame length = 275, 40 ms/frame length = 440, 60 ms/ frame length = 660, overlap frame: 0%, 25%, 40%, the number of codewords: 1 to 18, except for frame length 275 and overlap frame = 0 using 1 to 29 clusters. The identification of this type of infant cries uses the minimum distance of euclidean distance. Accuracy value is between 37% and 94%. Sound �eh� is the most familiar, whereas sound �owh� is always missunderstood and generally it is known as �neh� and �eairh�. The weakness point of this research is the silent is only be cut at the beginning and at the end of speech signal. Hopefully, in the next research, the silent can be cut in the middle of sound so that it can produce more specific sound. It has impact on the bigger accuracy as well.
关键词：Codebook; Dunstan baby language; Infant cries; K-means clustering; MFCC