C. Tadj, C. Gargour, L. Montazeri
This paper addresses the problem of automatic cry signal segmentation for the purposes of infant cry analysis. The main goal is to automatically detect expiratory and inspiratory phases from recorded cry signals. The approach used in this paper is made up of three stages: signal decomposition, features extraction and classification. Different decomposition techniques such as Short Time Fourier Transform, Empirical Mode Decomposition and Wavelet Packet Transform have been considered and various set of features have been obtained. Supervised classification approaches such as GMM and HMM with 4 and 5 states have been discussed as well. The experiments have been repeated several times for different training and testing data sets, randomly chosen using a 10-fold cross validation procedure. Global classification error rates of around 8.9% have been achieved.
Segmentation, Empirical Mode Decomposition, Wavelet Packet Transform,
Mel-Frequency Cepstral Coefficients, Gaussian Mixture Models, Hidden