Lina Abou-Abbas

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Traditional studies of infant cry signals focus more on non-pathology-based classification of infants. In this paper, we introduce a noninvasive health care system that performs acoustic analysis of unclean noisy infant cry signals to extract and measure certain cry characteristics quantitatively and classify healthy and sick newborn infants according to(More)
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(More)
Cry segmentation is an essential preprocessing step in any infant crying diagnosis system. Besides crying sounds consisting of expiration phases followed by short periods of inspiration episodes, each recording of newborn cries also includes silence sections as well as other sounds such as speech of caregivers, noise and sound of medical equipments. This(More)
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,(More)
The detection of cry sounds is generally an important pre-processing step for various applications involving cry analysis such as diagnostic systems, electronic monitoring systems, emotion detection, and robotics for baby caregivers. Given its complexity, an automatic cry segmentation system is a rather challenging topic. In this paper, a framework for(More)
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