Automatic detection of the expiratory and inspiratory phases in newborn cry signals

Abstract

An analysis of newborn cry signals, either for the early diagnosis of neonatal health problems or to determine the category of a cry (e.g., pain, discomfort, birth cry, and fear), requires a primary and preliminary preprocessing step 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 equipment) 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 in this report is able to detect the two basic constituents of a cry, which are the audible expiratory and inspiratory parts, using a two-stage recognition architecture. The system is trained and tested on a real database collected from normal and pathological newborns. The experimental results indicate that the system yields accuracies of up to 83.79%. extracted from the cries and the health problems of the child [2][3][4][5]. Various studies are currently under way to devise a tool that analyzes cries automatically, to diagnose neonatal pathologies [6][7][8].We are involved in the design of an automatic system for early diagnosis, called the Newborn Cry-based Diagnostic System (NCDS), which can detect certain pathologies in newborns at an early stage. The implementation of this system requires a database containing hundreds of cry signals. The overwhelming problem that arises when working with such a database is the diversity of acoustic activities that compose the audio recordings, such as background noise, speech, the sound of medical equipment and silence. Such diversity could harm the analysis process, as the presence of any acoustic component other than the cry itself could result in the misclassification of pathologies by reducing the NCDS system performance. This is because the NCDS would decode every segment of the recording signal, whether it is part of a cry or not. In this case, unwanted segment insertion in essential crying segments would lengthen the process of classification unnecessarily and leave the system prone to error. An important subtask of the NCDS is the manipulation of the newborn cry sound, and what is needed to perform this subtask is a segmentation system. Until now, few works have been carried out in this area. In this paper, we propose an automatic segmentation module designed to isolate the audible expiration and inspiration parts of cry sounds to serve as a preprocessing step of our NCDS. The rest of this paper is organized as follows: Related work is presented in section 2. The HMM and the HTK are reviewed briefly in section 3. The training corpus and the testing corpus are described in section 4. In section 5, the architect...

A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes

“…That is why we have given due consideration in our recent and present works to statistical model-based approaches (Abou-Abbas et al , 2015b; Abou-Abbas et al , 2015c). …”

“…We refer readers to the state of the art discussed in our recent works (Abou-Abbas et al , 2015b; Abou-Abbas et al , 2015a). …”

“…We recently developed different approaches for cry segmentation (Abou-Abbas et al , 2015b; Abou-Abbas et al , 2015c). The general structure of the systems designed can be described with a block diagram as shown in Fig.…”

“…In the works conducted previously (Abou-Abbas et al , 2017; Abou-Abbas et al , 2015b; Abou-Abbas et al , 2015c), studies were carried out to compare the accuracy rate of different feature vectors and classifiers. It has been shown that all of the proposed approaches mentioned in Table V allow for reliable discrimination of clean expiration from any other sounds.…”

A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices

“…There is a limited number of stress detection studies directly conducted for infants or children. Most of them utilized audio signal collected from children for crying detection [ 2 , 3 , 4 , 5 ], which is relatively easy to obtain. Using only audio signal for monitoring a child is not practical, as distinguishing the voice or sound of a child in real circumstances, where many children are gathered in one place, is almost impossible.…”