Infant Screening System Based on Cry Analysis (original) (raw)
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Statistical Vectors of Acoustic Features for the Automatic Classification of Infant Cry
International Journal of Information Acquisition, 2007
With the objective of helping diagnose some pathologies in recently born babies, we present the experiments and results obtained in the classification of infant cry using a variety of single classifiers, and ensembles from the combination of them. Three kinds of cry were classified: normal, hypoacoustic (deaf), and asphyxia. The feature vectors were formed by the extraction of Mel Frequency Cepstral Coefficients (MFCC). The vectors were then processed and reduced through the application of five statistics operations, namely: minimum, maximum, average, standard deviation and variance. LDA, a data reduction technique is implemented with the purpose of comparing the results of our proposed method. Four supervised machine learning methods including Support Vector Machines, Neural Networks, J48, Random Forest and Naive Bayes are used. The ensembles tested were combinations of these under different approaches like Majority Vote, Staking, Bagging and Boosting.
A review of infant cry analysis and classification
EURASIP Journal on Audio, Speech, and Music Processing, 2021
This paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous researc...
Biomedical Signal Processing and Control
In the infant cry analysis, the identification of qualitative features is of great importance, because this provides relevant information to differentiate between normal and pathological cries, which makes important their identification. Qualitative infant cry analysis has been done until now by medical personal through visual inspection of spectrograms and by the auditory study of the cry recordings. In this way, the success of the process depends on the subjective perception of the inspector besides being a very slow task. The information extracted from the perceptive observation of the crying waves recordings is then used as a help to emit diagnosis. With the idea of helping to make the whole process easier and faster we are developing a method to automatically identify, measure and highlight selected qualitative features in infant cry recordings. The processing of this identifier starts with the automatic discovery of infant cry units, which is performed by the use of a threshold applied to the energy of the signal along with another threshold applied to eliminate inspiratory cry segments, when not needed. From all the detected cry units, the process automatically identifies melodic shape, shifts, glides and noise concentration. In this work, we present, besides a quick review of related works, and a description of the perceptive analysis to help diagnosis, the process implementation, some experiments as well as the experimental results obtained.
Identification of diseases in newborns using advanced acoustic features of cry signals
Biomedical Signal Processing and Control, 2019
Our challenge in the current study is to extend research on the cries of newborns for the early diagnosis of different pathologies. This paper proposes a recognition system for healthy and pathological cries using a probabilistic neural network classifier. Two different kinds of features have been used to characterize newborn cry signals: 1) acoustic features such as fundamental frequency glide (F 0glide) and resonance frequencies dysregulation (RFs dys); 2) conventional features such as mel-frequency cestrum coefficients. This paper describes the automatic estimation of the proposed characteristics and the performance evaluation of these features in identifying pathological cries. The adopted methods for F 0glides and RFs dys estimation are based on the derived function of the F 0 contour and the jump "J" of the RFs between two subsequent tunings, respectively. The database used contains 3250 cry samples of full-term and preterm newborns, and includes healthy and pathologic cries. The obtained results indicate the important association between the quantified features and some studied pathologies, and also an improvement in the identification of pathologic cries. The best result obtained is 88.71% for the correct identification of health status of preterm newborns, and 82% for the correct identification of full-term infants with a specific disease. We conclude that using the proposed characteristics improves the diagnosis of pathologies in newborns. Moreover, the method applied in the estimation of these characteristics allows us to extend this study to other uninvestigated pathologies.
Infant Cry Analysis and Detection
—In this paper we propose an algorithm for automatic detection of an infant cry. A particular application of this algorithm is the identification of a physical danger to babies, such as situations in which parents leave their children in vehicles. The proposed algorithm is based on two main stages. The first stage involves feature extraction, in which pitch related parameters, MFC (mel-frequency cepstrum) coefficients and short-time energy parameters are extracted from the signal. In the second stage, the signal is classified using the k-NN algorithm and is later verified as a cry signal, based on the pitch and harmonics information. In order to evaluate the performance of the algorithm in real world scenarios, we checked the robustness of the algorithm in the presence of several types of noise, and especially noises such as car horns and car engines that are likely to be present in vehicles. In addition, we addressed real time and low complexity demands during the development of the algorithm. In particular, we used a voice activity detector, which disabled the operation of the algorithm when voice activity was not present. A database of baby cry signals was used for performance evaluation. The results showed good performance of the proposed algorithm, even at low SNR.
A Database of Infant Cry Sounds to Study the Likely Cause of Cry
2015
Infant cry is a mode of communication, for interacting and drawing attention. The infants cry due to physiological, emotional or some ailment reasons. Cry involves high pitch changes in the signal. In this paper we describe an ‘Infant Cry Sounds Database’ (ICSD), collected especially for the study of likely cause of an infant’s cry. The database consists of infant cry sounds due to six causes: pain, discomfort, emotional need, ailment, environmental factors and hunger/thirst. The ground truth cause of cry is established with the help of two medical experts and parents of the infants. Preliminary analysis is carried out using the sound production features, the instantaneous fundamental frequency and frame energy derived from the cry acoustic signal, using auto correlation and linear prediction (LP) analysis. Spectrograms give the base reference. The infant cry sounds due to pain and discomfort are distinguished. The database should be helpful towards automated diagnosis of the causes...
On the automatic audio analysis and classification of cry for infant pain assessment
International Journal of Speech Technology, 2019
The effectiveness of pain management relies on the choice and the correct use of suitable pain assessment tools. In the case of newborns, some of the most common tools are human-based and observational, thus affected by subjectivity and methodological problems. Therefore, in the last years there has been an increasing interest in developing an automatic machine-based pain assessment tool. This research is a preliminary investigation towards the inclusion of a scoring system for the vocal expression of the infant into an automatic tool. To this aim we present a method to compute three correlated indicators which measure three distress-related features of the cry: duration, dysphonantion and fundamental frequency of the first cry. In particular, we propose a new method to measure the dysphonantion of the cry via spectral entropy analysis, resulting in an indicator that identifies three well separated levels of distress in the vocal expression. These levels provide a
Newborn's pathological cry identification system
2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012
In this paper we compare the performance of an identification system of the pathological and normal cries of the newborn, using various methods of characterisation of cries. This system is similar to a speaker identification system. It contains two main parts namely a cry signal characterisation and modeling. We used Mel-Frequency Cestrum Coefficients and Mel Frequency Discret Wavelet Coefficients to characterize the newborn cry signals. We also applied Best Structure Abstract Tree algorithm and the Principal Component Analysis to reduce the number of Wavelet packet transform WPT coefficients. In this study a Probabilistic Neural Network classifier is used. The best result obtained is 96.
Proceedings of the 9th …, 2004
In this work we present the design of an automatic infant cry recognition system that classifies three different kinds of cries, which come from normal, deaf and asphyxiating infants, of ages from one day up to nine months old. The classification is done through a pattern classifier, where the crying waves are taken as the input patterns. We have experimented with patterns formed by vectors of Mel Frequency Cepstral Coefficients and Linear Prediction Coefficients. The acoustic feature vectors are then processed, to be classified in their corresponding type of cry, through an Input Delay Neural Network, trained by gradient descent with adaptive learning rate back propagation algorithm. To perform the experiments and to test the recognition system, we train the neural network with cries from randomly selected babies, and test it with a separate set of cries from babies selected only for testing. Here, we present the design and implementation of the complete system, as well as the results from some experiments, which in the presented case are up to 86 %.