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.