Identification of Infants’ Cry Motivation Using Spectrograms

Felipe, Gustavo Z.; Aguiat, Rafael L.; Costa, Yandre M. G.; Silla, Carlos N.; Brahnam, Sheryl; Nanni, Loris; McMurtrey, Shannon

doi:10.1109/iwssip.2019.8787318

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…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). Furthermore, Osmani et al have illustrated that boosted and bagging trees outperform SVM cry classification (24).…”

Section: Discussionmentioning

confidence: 99%

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%

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

et al. 2021

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“…Zhang et al created new waveform images from training datasets by transforming these waveform images into slightly faster or slightly slower waveforms for the purpose of increasing training datasets to overcome overfitting problem [12]. In [43], several data augmentation techniques, such as noise variation, signal intensity variation, tonality variation, and spectrogram's size alteration, were used to artificially increase either the number of audio signals or the number of spectrograms. The experimental results showed that these data augmentation methods cannot lead to accuracy improvement.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In [24], Singh et al explored the residual MFCC and implicit LP residual features that represent excitation source information. Researchers have also tried other cepstral features such as Fast Fourier Transform (FFT) [23,66], Log-Mel feature [11,18], Mel Scale [43], Constant-Q Chromagram [43], Log-mel spectrum [12], and delta spectrum [12]. According to auditory perception models, MFCC coefficients are more robust than other coefficients such as LPC coefficients.…”

Section: Cepstral Domain Featuresmentioning

confidence: 99%

“…Instead of using zero padding to achieve same length of feature vectors, normalization is applied in the process of spectrogram generation, which produces the same size images without changing the original signal. Besides feeding the spectrogram into CNN [9,35,48,50] and capsule neural network [41], researchers take extra step to use the spectrogram image to retrieve extra features such as Local Binary Pattern (LBP), Local Phase Quantization (LPQ), and Robust Local Binary Pattern (RLBP) [43] to help improve the classification performance.…”

Section: Image Domain Featuresmentioning

confidence: 99%

“…The most popular probabilistic classifier used in infant cry classification is Support Vector Machine (SVM) [26,40,43]. Many machine learning methods have been experimented in infant research.…”

Section: Infant Cry Classification Models 411 Traditional Machine Lmentioning

confidence: 99%

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A review of infant cry analysis and classification

Mudiyanselage

Gao

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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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 research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

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Future roles of artificial intelligence in early pain management of newborns

Salekin

Mouton²,

Patel

et al. 2021

Paediatric and Neonatal Pain

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The advent of increasingly sophisticated medical technology, surgical interventions, and supportive healthcare measures is raising survival probabilities for babies born premature and/or with life‐threatening health conditions. In the United States, this trend is associated with greater numbers of neonatal surgeries and higher admission rates into neonatal intensive care units (NICU) for newborns at all birth weights. Following surgery, current pain management in NICU relies primarily on narcotics (opioids) such as morphine and fentanyl (about 100 times more potent than morphine) that lead to a number of complications, including prolonged stays in NICU for opioid withdrawal. In this paper, we review current practices and challenges for pain assessment and treatment in NICU and outline ongoing efforts using Artificial Intelligence (AI) to support pain‐ and opioid‐sparing approaches for newborns in the future. A major focus for these next‐generation approaches to NICU‐based pain management is proactive pain mitigation (avoidance) aimed at preventing harm to neonates from both postsurgical pain and opioid withdrawal. AI‐based frameworks can use single or multiple combinations of continuous objective variables, that is, facial and body movements, crying frequencies, and physiological data (vital signs), to make high‐confidence predictions about time‐to‐pain onset following postsurgical sedation. Such predictions would create a therapeutic window prior to pain onset for mitigation with non‐narcotic pharmaceutical and nonpharmaceutical interventions. These emerging AI‐based strategies have the potential to minimize or avoid damage to the neonate's body and psyche from postsurgical pain and opioid withdrawal.

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Identification of Infants’ Cry Motivation Using Spectrograms

Cited by 28 publications

References 17 publications

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

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

A review of infant cry analysis and classification

Future roles of artificial intelligence in early pain management of newborns

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