Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders

Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the Student t-test. The empirical results show that both expiration and inspiration signals exhibit clear evidence of multifractal properties under healthy and unhealthy conditions. In addition, expiration and inspiration signals exhibit more complexity under healthy conditions than under unhealthy conditions. Furthermore, distributions of multifractal characteristics are different across healthy and unhealthy conditions. Hence, this study improves the understanding of infant crying by providing a complete description of its intrinsic dynamics to better evaluate its health status.

Expiratory and Inspiratory Cries Detection Using Different Signals' Decomposition Techniques

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. In the first stage, short-time Fourier transform, empirical mode decomposition (EMD), and wavelet packet transform have been considered. In the second stage, various set of features have been extracted, and in the third stage, two supervised learning methods, Gaussian mixture models and hidden Markov models, with four and five states, have been discussed as well. The main goal of this work is to investigate the EMD performance and to compare it with the other standard decomposition techniques. A combination of two and three intrinsic mode functions (IMFs) that resulted from EMD has been used to represent cry signal. The performance of nine different segmentation systems has been evaluated. The experiments for each system have been repeated several times with different training and testing datasets, randomly chosen using a 10-fold cross-validation procedure. The lowest global classification error rates of around 8.9% and 11.06% have been achieved using a Gaussian mixture models classifier and a hidden Markov models classifier, respectively. Among all IMF combinations, the winner combination is IMF3+IMF4+IMF5.

Subjective quantification of earplug occlusion effect using external acoustical excitation of the mouth cavity

Occlusion of the ear canal by hearing aids or hearing protectors often results in an occlusion effect, which creates a discomfort to wearers in that it changes their perception of their own voice. As no account was found in the literature on the quantification of this subjective voice occlusion effect, an experimental method is proposed based on the use of an artificial sound source emitting within the subject's mouth to replace his own voice. A block diagram is constructed to identify the different internal sound path components involved in the perception of one's own voice and is used to show that the subjective voice occlusion effect is the weighted energy summation of two components. The first component, the voice air and body conduction occlusion effect for which data is obtained from the experiments reported in the present paper, constitute the lower limit of the subjective voice occlusion effect. The second component, the voice body conduction occlusion effect for which data is available in the literature, constitutes the upper limit. From these limits, order of magnitudes for subjective voice occlusion effect intervals are estimated to be [+5+20] dB below 2000 Hz and [-10+5] dB above 2000 Hz.