L. Abou-Abbas, C. Tadj
An analysis of newborn cry signals, either for the early diagnosis of neonatal health problems or to determine the category of a cry (pain, discomfort, birth cry, fear, etc.), requires a primary and preliminary preprocessing step in order to quantify the important expiratory and inspiratory parts of the audio recordings of newborn cries. Data typically contain clean cries interspersed with sections of other sounds (generally the sounds of speech, noise, or medical equipments) or silence. The purpose of signal segmentation is to differentiate the important acoustic parts of the cry recordings from the unimportant acoustic activities that compose the audio signals. This paper reports on our research to establish an automatic segmentation system for newborn cry recordings based on Hidden Markov Models using the HTK (Hidden Markov Model Toolkit). The system presented here is able to detect the two basic constituents of a cry, which are the audible expiratory and inspiratory parts, using two-stage recognition architecture. The system is trained and tested on a real-time database collected from normal and pathological newborns. The experimental results indicate that the system yields accuracies of up to 83%.
Keywords: HMM; HTK;
Automatic segmentation; Newborn cry signals; Mel Frequency Cepstrum
coefficients; Viterbi algorithm; Baum Welch algorithm.