Segmentation of voiced newborns' cry sounds using wavelet packet based features

This paper proposes a method for the segmentation of newborn's cry signals recorded in real conditions using the Teager-Kaiser energy operator (TKEO). Based on the wavelet packet analysis, the audio signals are divided into different frequency channels, and then the TKEO and the energy are estimated within each band. The Hidden Markov Models have been used as a classification tool to distinguish the voiced cry parts from the irrelevant acoustic activities that compose the audio signals. The proposed method divided the audio signal containing newborns' cry sounds into different periods showing the audible Expiration and Inspiration of the cry. Different levels of wavelet packet transform have been used to verify the performance of the proposed method on crying signals segmentation and have shown that based on wavelet packet decomposition, the TKEO measure is more effective than the traditional energy measure in detecting important parts of cry signal in a very noisy environment. The proposed features have shown to achieve an accuracy rate of 84.08 %.