A review: survey on automatic infant cry analysis and classification

Jeyaraman, Saraswathy; Hariharan, M.; Khairunizam, Wan; Jeyaraman, Sarojini; Nadarajaw, Thiyagar; Yaacob, Sazali; Nisha, S. Pathur

doi:10.1007/s12553-018-0243-5

Cited by 18 publications

(9 citation statements)

References 69 publications

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“…Traditional machine learning classifiers and neural network-based classifiers have been used for infant cry analysis and classification (21). We found that several studies that explored the use of minimum, maximum, mean, standard deviation and the variance of MFCCs and other audio features to differentiate normal, hypo-acoustic and asphyxia types using the Chillanto database (6). Support Vector Machines (SVM) are among the most popular infant classification algorithms and routinely outperform neural network classifiers (22,23).…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, a classification algorithm is necessary for the automatic recognition of cries in audio files. Given the importance for researchers to study the relationship between an infant's crying patterns and their health, automatic detection and quantification of infant cries from an audio signal is an essential step in remote baby monitoring applications (6).…”

Section: Introductionmentioning

confidence: 99%

“…Automatic cry detection has been reported in the form of remote baby monitors for non-intrusive clinical assessments of infants in hospital settings (6)(7)(8)(9), and several researchers have shown that classification of cry-and non-cry-sounds is possible with machine-learning algorithms (10)(11)(12). However, most algorithms lack validation in a completely independent dataset, which is crucial to predict performance in new-and real-world settings, while data regarding intra-and inter-device variability and other factors that may influence repeatability is lacking as well (10,13,14).…”

Section: Introductionmentioning

confidence: 99%

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Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm

et al. 2021

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Introduction: The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labeling of crying is laborious, subjective, and sometimes inaccurate. The aim of this study was to develop and technically validate a smartphone-based algorithm able to automatically detect crying.Methods: For the development of the algorithm a training dataset containing 897 5-s clips of crying infants and 1,263 clips of non-crying infants and common domestic sounds was assembled from various online sources. OpenSMILE software was used to extract 1,591 audio features per audio clip. A random forest classifying algorithm was fitted to identify crying from non-crying in each audio clip. For the validation of the algorithm, an independent dataset consisting of real-life recordings of 15 infants was used. A 29-min audio clip was analyzed repeatedly and under differing circumstances to determine the intra- and inter- device repeatability and robustness of the algorithm.Results: The algorithm obtained an accuracy of 94% in the training dataset and 99% in the validation dataset. The sensitivity in the validation dataset was 83%, with a specificity of 99% and a positive- and negative predictive value of 75 and 100%, respectively. Reliability of the algorithm appeared to be robust within- and across devices, and the performance was robust to distance from the sound source and barriers between the sound source and the microphone.Conclusion: The algorithm was accurate in detecting cry duration and was robust to various changes in ambient settings.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm

et al. 2021

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“…Differences of cry between normal babies and those with high-risk for autism have been studied as well [59,60]. A recent survey of the research status is available [61].…”

Section: The Origin and Role Of Infant Crymentioning

confidence: 99%

Development and Analysis of Deep Learning Architectures

2020

Studies in Computational Intelligence

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In this chapter, we compare deep learning and classical approaches for detection of baby cry sounds in various domestic environments under challenging signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network (CNN) architectures for baby cry detection, and compare their performance to that of classical machine-learning approaches, such as logistic regression and support vector machines. In addition to feedforward CNNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized nonsymmetric kernels, better results are obtained compared to common CNN architectures.

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“…Machine learning enables systems to automatically learn and build the analytical model from their experience. [ 1 – 14 ] The work in [ 15 ] presented an extensive review of research done focusing on the analysis and classification of infant crises. The review was conducted targeting various aspects such as data acquisition techniques, cross-domain signal processing techniques, and various machine learning classification techniques.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network

Chang

Bhattacharya

Vincent

et al. 2021

Journal of Healthcare Engineering

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The cry is a loud, high pitched verbal communication of infants. The very high fundamental frequency and resonance frequency characterize a neonatal infant cry having certain sudden variations. Furthermore, in a tiny duration solitary utterance, the cry signal also possesses both voiced and unvoiced features. Mostly, infants communicate with their caretakers through cries, and sometimes, it becomes difficult for the caretakers to comprehend the reason behind the newborn infant cry. As a result, this research proposes a novel work for classifying the newborn infant cries under three groups such as hunger, sleep, and discomfort. For each crying frame, twelve features get extracted through acoustic feature engineering, and the variable selection using random forests was used for selecting the highly discriminative features among the twelve time and frequency domain features. Subsequently, the extreme gradient boosting-powered grouped-support-vector network is deployed for neonate cry classification. The empirical results show that the proposed method could effectively classify the neonate cries under three different groups. The finest experimental results showed a mean accuracy of around 91% for most scenarios, and this exhibits the potential of the proposed extreme gradient boosting-powered grouped-support-vector network in neonate cry classification. Also, the proposed method has a fast recognition rate of 27 seconds in the identification of these emotional cries.

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A review: survey on automatic infant cry analysis and classification

Cited by 18 publications

References 69 publications

Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm

Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm

Development and Analysis of Deep Learning Architectures

An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network

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