Newborn’s Pathological Cry Identication Ssystem,
In
this paper we compare the performance of an identification system of
the pathological and normal cries of the newborn, using various methods
of characterisation of cries. This system is similar to a speaker
identification system. It contains two main parts namely a cry signal
characterisation and modeling. We used Mel-Frequency Cestrum
Coefficients and Mel Frequency Discret Wavelet Coefficients to
characterize the newborn cry signals. We also applied Best Structure
Abstract Tree algorithm and the Principal Component Analysis to reduce
the number of Wavelet packet transform WPT coefficients. In this study
a Probabilistic Neural Network classifier is used. The bestresult
obtained is 96.99 % of correct identification using Best Structure
Abstract Tree algorithm.
Keywords: Classification, pathologic cry, WPT, PCA, Best abstract Tree