Newborn's pathological cry identification system (original) (raw)
Related papers
Infant Cry Classification Using Dual Tree Complex Wavelet Transform Features
Advanced Science Letters, 2018
Cry is the only communication of infant when they response to certain situations and the cry signals can be used to identify the status of babies. In this paper, Dual Tree Complex Wavelet Transform (DT-CWT) had been utilized to investigate the cry signals and understand the hidden message of such signals. Total of 10 energy and Shannon entropy features were extracted after five levels of decomposition by using DT-CWT. Three sets of pairwise classification (asphyxia versus normal (A vs. N), deaf versus normal (D vs. N), and hunger versus pain (H vs. P)) were conducted in this paper with different feature sets. Least-Square Support Vector Machine (LS-SVM) and Extreme Learning Machine (ELM) were used to measure the efficacy of the proposed method in distinguishing the different classes. The proposed DT-CWT feature extraction and classification methods achieved high accuracies of 92.79%, 99.49%, and 83.02% for asphyxia versus normal, deaf versus normal, and hunger versus pain respectively.
Pathological infant cry analysis using wavelet packet transform and probabilistic neural network
Expert Systems with Applications, 2011
A new approach has been presented based on the wavelet packet transform and probabilistic neural network (PNN) for the analysis of infant cry signals. Feature extraction and development of classification algorithms play important role in the area of automatic analysis of infant cry signals. Infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy measures are extracted at every level of decomposition and they are used as features to quantify the infant cry signals. A PNN is developed to classify the infant cry signals into normal and pathological and trained with different spread factor or smoothing parameter to obtain better classification accuracy. The experimental results demonstrate that the proposed features and classification algorithms give very promising classification accuracy of 99% and it proves that the proposed method can be used to help medical professionals for diagnosing pathological status of an infant from cry signals.
An investigation into classification of infant cries using modified signal processing methods
2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), 2015
Infant cry is a biological signal through which an infant communicates with its care-giving environment. It also contains valuable information about the state of the infant. Infants produce this sound in response to a stimuli, which could be pain, discomfort, emotional need of attention, ailment, environmental factors or hunger/thirst. Signal processing methods that work well for adults are not adequate in the case of infants. In order to analyze the infant cry signals, these methods require some modifications. Signal processing methods such as short-time Fourier transform, auto-correlation and linear prediction analysis are modified and used. Features such as frame-energy and fundamental frequency are extracted from the cry signal. An Infant Cry and Causes (ICC) database especially collected for the study is used. Ground truth information about the fundamental frequency (F0) is obtained using the spectrograms. The fluctuations in F0 are examined using mean and standard deviation, for different causes of cry. The results about fluctuations in F0 obtained from three different signal processing methods are compared. Objective is to classify the infant cry sounds into different categories of causes, based on the features derived from the infant cry signal. The results of the limited analysis are encouraging.
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.
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.
TENCON 2014 - 2014 IEEE Region 10 Conference, 2014
This paper is about the creation of an artificial neural network (ANN) in MATLAB to analyze the features extracted from calculating the mel-frequency cepstral coefficients (MFCC) of the raw audio data. The paper explains basic concepts about the ANN, as well as the MFCC and other relevant theories. Regarding the design of the ANN, it uses multiple infant crying sounds, as well as non-crying sounds, to create a sample training set with a corresponding target that determines whether the sound is a cry or not. The paper uses relevant concepts heavily utilized in speech recognition for the design of the infant cry recognition, modifies them, and adds a few more calculations to fit the desired application to compensate for the differences present in a cry from human speech.
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 %.
Infants Cry Classification of Physiological State Using Cepstral and Prosodic Acoustic Features
Journal of Telecommunication, Electronic and Computer Engineering, 2018
Infants cry to express their emotional, psychological and physiological states. The research paper investigates if cepstral and prosodic audio features are enough to classify the infants’ physiological states such as hunger, pain and discomfort. Dataset from our previous paper was used to train the classification algorithm. The results showed that the audio features could classify an infant’s physiological state. We used three classification algorithms, Decision Tree (J48), Neural Network and Support Vector Machine in developing the infant physiological model. To evaluate the performance of the infant physiological state model, Precision, Recall and F-measure were used as performance metrics. Comparison of the cepstral and prosodic audio feature is presented in the paper. Our findings revealed that Decision Tree and Multilayer Perceptron performed better both for cepstral and prosodic feature. It is noted the cepstral feature yielded better result compare with prosodic feature for t...
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...
Infant Screening System Based on Cry Analysis
2018
Acoustical investigation of infant cries has been a clinical and research focus in the recent years. Findings of several studies reveal the importance of cry as a useful window for early detection of several diseases and communication difficulties such as hearing impairment, intellectual disabilities, cerebral palsy etc. This motivates us to use a minimal interface system that can automatically classify infant cries into normal and pathological with the help of state-of-the-art machine learning strategies. In this paper, we propose a software program for screening infants based on their cries. The proposed system is able to detect & classify infant cries into normal and pathological based on the acoustic input. To build and train the system, infant cries of normal and Low Birth Weight (LBW) newborn within 7 days of birth were considered. A pain induced cry elicited using the routine intramuscular immunization was recorded using a standard Olympus LS-100 recorder which was held about...