Infant Cry Research Articles

Kajian Kriminologi Terhadap Kasus Pembunuhan Seorang Bayi Oleh Ibu Kandung

Unwanted pregnancies often end in unlawful acts such as illegal abortions, murder, or the disposal of the baby by the birth mother.
 Therefore, this study aims to find out "How is the chronology of the perpetrator (who is a mother) to kill a baby who is her flesh and blood?" and "What is the driving factor for the crime of infanticide by the birth mother?".
 To obtain data, the writer conducted an interview with Mrs. Ratih Mannul Izzati, SH., MH. as the Panel of Judges of the Magelang District Court and conducted a study on the Decision on Case Number 16/Pid.Sus/2021/PN Mgg.
 The results of this study indicate that the chronology of the perpetrators of killing their babies by trying to have an abortion in early pregnancy but without success. Then on the day the baby was born, the perpetrator killed the baby by putting camphor into the baby's mouth and then strangling it until it suffocated; and the driving factor for the perpetrator of the murder of the baby was a pregnancy out of wedlock which resulted in fear of an illicit relationship with his lover being exposed, because the perpetrator was an only child who was the hope of his parents, besides that the perpetrator was also a victim of the irresponsibility of the man who impregnated him, the reason economy, and panic when finding a newborn baby crying.

Motivational increase of androgens and behavior by infant distress calls in highly responsive common marmoset fathers, Callithrix jacchus

Common marmoset fathers are highly involved in care of their infants. However, variability exists in their response to infant behavior even in paternally experienced fathers. Using infant distress cries as a motivation test, we investigated: 1. the differences in paternally experienced fathers' motivation to search for the infant vocalization stimuli; 2. the relationship between a father's motivation to search for the source of the infant cries and testosterone levels; and 3. if there is a rapid steroidogenesis pathway leading to increased testosterone and estradiol in the peripheral circulation. Only 44% of the paternally experienced fathers showed a high frequency of searching for the source of the infant distress cries. Through the use of multisteroid analysis, we found high responsive fathers had significantly higher levels of progesterone and testosterone in response to infant distress cries compared to a control stimulus with progesterone and androstenedione correlating with testosterone, while no differences were seen in low responders. The frequency to search for the infant stimuli was positively correlated with higher testosterone compared to control vocal levels. These results suggest that searching for the source of infant cries represents a motivation behavior for fathers that is activated by testosterone and reflects rapid circulating testosterone.

Ensemble of multimodal deep learning autoencoder for infant cry and pain detection

Background: Babies cannot communicate their pain properly. Several pain scores are developed, but they are subjective and have high variability inter-observer agreement. The aim of this study was to construct models that use both facial expression and infant voice in classifying pain levels and cry detection. Methods: The study included a total of 23 infants below 12-months who were treated at Dr Soetomo General Hospital. The the Face Leg Activity Cry and Consolability (FLACC) pain scale and recordings of the baby's cries were taken in the video format. A machine-learning-based system was created to detect infant cries and pain levels. Spectrograms with the Short-Time Fourier Transform were used to convert the audio data into a time-frequency representation. Facial features combined with voice features extracted by using the Deep Learning Autoencoders was used for the classification of infant pain levels. Two types of autoencoders: Convolutional Autoencoder and Variational Autoencoder were used for both faces and voices. Result: The goal of the autoencoder was to produce a latent-vector with much smaller dimensions that was still able to recreate the data with minor losses. From the latent-vectors, a multimodal data representation for Convolutional Neural Network (CNN) was used for producing a relatively high F1 score, higher than single data modal such as the voice or facial expressions alone. Two major parts of the experiment were: 1. Building the three autoencoder models, which were autoencoder for the infant’s face, amplitude spectrogram, and dB-scaled spectrogram of infant’s voices. 2. Utilising the latent-vector result from the autoencoders to build the cry detection and pain classification models. Conclusion: In this paper, four pain classifier models with a relatively good F1 score was developed. These models were combined by using ensemble methods to improve performance, which resulted in a better F1 score.

A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification.

Understanding the reason for an infant's cry is the most difficult thing for parents. There might be various reasons behind the baby's cry. It may be due to hunger, pain, sleep, or diaper-related problems. The key concept behind identifying the reason behind the infant's cry is mainly based on the varying patterns of the crying audio. The audio file comprises many features, which are highly important in classifying the results. It is important to convert the audio signals into the required spectrograms. In this article, we are trying to find efficient solutions to the problem of predicting the reason behind an infant's cry. In this article, we have used the Mel-frequency cepstral coefficients algorithm to generate the spectrograms and analyzed the varying feature vectors. We then came up with two approaches to obtain the experimental results. In the first approach, we used the Convolution Neural network (CNN) variants like VGG16 and YOLOv4 to classify the infant cry signals. In the second approach, a multistage heterogeneous stacking ensemble model was used for infant cry classification. Its major advantage was the inclusion of various advanced boosting algorithms at various levels. The proposed multistage heterogeneous stacking ensemble model had the edge over the other neural network models, especially in terms of overall performance and computing power. Finally, after many comparisons, the proposed model revealed the virtuoso performance and a mean classification accuracy of up to 93.7%.

The effects of oxytocin and vasopressin administration on fathers’ neural responses to infant crying: A randomized controlled within-subject study

In a randomized double-blind within-subject control study we investigated the effects of oxytocin and vasopressin administration on neural reactivity to infant cry sounds in 70 first-time fathers in the first year of fatherhood. Additionally, we examined whether effects of oxytocin and vasopressin administration on neural reactivity were moderated by fathers’ early childhood experiences. Neural reactivity to infant cry sounds (versus control sounds) was measured using functional magnetic resonance imaging (fMRI). Furthermore, participants reported on their childhood experiences of parental harsh discipline and parental love withdrawal. Whole brain analyses revealed no significant effect of vasopressin or oxytocin administration on neural activation in response to infant cry sounds. Region of interest analyses showed decreased amygdala activation in both the oxytocin condition and the vasopressin condition as compared to placebo. We found no moderating effects of fathers’ early childhood experiences. Our findings suggest that oxytocin administration may decrease feelings of anxiety or aversion to a crying infant. Whether decreased amygdala activation after vasopressin administration might be explained by contextual factors (e.g., absence of high levels of threat, unfamiliarity of the infant) or represents an affiliative response to infant distress warrants further investigation. Findings of the present study showed that oxytocin and vasopressin are important hormones implicated in neural models of infant cry perception in fatherhood.

Minor second intervals: A shared signature for infant cries and sadness in music.

In Western music and in music of other cultures, minor chords, modes and intervals evoke sadness. It has been proposed that this emotional interpretation of melodic intervals (the distance between two pitches, expressed in semitones) is common to music and vocal expressions. Here, we asked expert musicians to transcribe into music scores spontaneous vocalizations of pre-verbal infants to test the hypothesis that melodic intervals that evoke sadness in music (i.e., minor 2nd) are more represented in cry compared to neutral utterances. Results showed that the unison, major 2nd, minor 2nd, major 3rd, minor 3rd, perfect 4th and perfect 5th are all represented in infant vocalizations. However, minor 2nd outnumbered all other intervals in cry vocalizations, but not in neutral babbling. These findings suggest that the association between minor intervals and sadness may develop in humans because a critically relevant social cue (infant cry) contains a statistical regularity: the association between minor 2nd and negative emotional valence.

Dealing With Autocorrelation in the Single Case AB-Design: A Study Assessing the Mediating Effect of Infant Crying on the Relation Between Responsive Soothing and Infant Sleep

Mediation analysis is widely adopted by researchers to disentangle the causal pathways by which an intervention affects an outcome. This paper describes a model to estimate the direct and indirect effect from a single subject AB-design with repeated assessments of both the mediator and the outcome. We discuss the plausibility of the modeling assumptions and contrast different approaches to deal with the autocorrelation in the time series data. While there are only small differences between those approaches when the number of timepoints is small (T = 15), the Generalized Least Squares approach performs best in medium (T = 30) to large (T = 90) time series. We apply the proposed methodology to data from a single case AB-design that was conducted with a mother of an excessively crying baby. Daily crying and baby sleep during the night were assessed with online diaries during a baseline phase and intervention phase. Between both phases, the pediatrician instructed the mother how to apply a responsive soothing intervention, the happiest baby (THB) method. We find that the direct effect of THB on sleeping is positive. THB also reduces crying but decreased crying during the day is associated with decreased sleeping during the night and hence a negative indirect effect of THB on sleeping via crying is found.

A Deep Convolutional Neural Network Based Approach for Effective Neonatal Cry Classification

Background: The cry is the universal language for babies to communicate with others. Infant cry classification is a kind of speech recognition problem that should be treated wisely. In the last few years, it has been gaining its momentum which will be very helpful for the caretaker. Objective: This study aims to develop infant cry classification system predictive model by converting the audio signals into spectrogram image then implementing deep convolutional neural network. It performs end to end learning process and thereby reducing the complexity involved in audio signal analysis and improves the performance using optimization technique. Method: A time frequency-based analysis called Short Time Fourier Transform (STFT) is applied to generate the spectrogram. 256 DFT (Discrete Fourier Transform) points are considered to compute the Fourier transform. A Deep convolutional neural network called AlexNet with few enhancements is done in this work to classify the recorded infant cry. To improve the effectiveness of the above mentioned neural network, Stochastic Gradient Descent with Momentum (SGDM) is used to train the algorithm. Results: A deep neural network-based infant cry classification system achieves a maximum accuracy of 95% in the classification of sleepy cries. The result shows that convolutional neural network with SGDM optimization acquires higher prediction accuracy. Conclusion: Since this proposed work is compared with convolutional neural network with SGD and Naïve Bayes and based on the result, it is implied the convolutional neural network with SGDM performs better than the other techniques.

Efficacy of Aromatherapy for Night Crying in Infants with Infantile Colic: A Double-Blind Randomized Controlled Trial.

Too much crying is a self-limiting problem and disappears within a few weeks. However, it can lead to maternal depression and parental stress. This study aimed to evaluate the effect of lavender oil inhalation on the duration of night crying within a week of intervention among infants with infantile colic. In this double-blind randomized clinical trial, the inclusion criteria for infants were being healthy, not taking any medications for colic, having an episode of crying two hours a day, and having a healthy mother. The intervention group received lavender oil inhalation and the control group received sweet almond oil inhalation for seven days. The data pertaining to the duration of crying were collected four times a day (morning, afternoon, evening, and night) via telephone contact. Mothers' mood scores were also assessed at the beginning of the study and on the seventh day of the intervention. At first, the two groups were not different in terms of the duration of crying. After the intervention, however, a significant difference was observed in this regard on all seven days of the study (P < 0.001). Based on the results of the repeated measures analysis, the difference between the two groups was statistically significant (P < 0.001). The results showed that inhalation of lavender oil at low concentrations could reduce colic symptoms and improve maternal mood.

Design and Fabrication of Arduino-Based Automated Cradle Rocking and Moisture Detection Mechanism

Anew, inexpensive domestic electronic cradle that can compete with currentimported, expensive models has been a long-time need. This study describes thedesign and implementation of a new indigenous low-cost E-Baby Cradle thatswings automatically when the baby cries. The cry analyzing system recognizesthe sound of a baby's cry and swings the cradle appropriately until the babystops crying. According to the user's needs, the cradle's speed can be adjusted.The cradle also has an integrated alarm that signals two situations: First,when the mattress is wet, which is a crucial factor in maintaining the baby'shygienic state; second, when the infant continues to cry for a prolongedperiod, which suggests that the baby requires attention. This method enablesnurses and parents to care for infants without having to physically touch them.

Tips for Coping with a New Baby During COVID-19

All babies cry. Most babies cry a lot from two weeks to two months of age. Some cry more than others, and some cry longer than others. For many new parents, crying is one of the most stressful parts of coping with a newborn.In some cases, extreme stress and a temporary lapse of emotional control in a caregiver can lead to actions that result in abusive head trauma. In fact, the most common trigger for abusive head trauma is simply a crying baby. As an American Academy of Pediatrics policy statement points out, actions that lead to abusive head trauma often happen when caretakers experience stress and get to the end of their rope.However, there is a bright side: The key to preventing abusive head trauma is to understand how common feelings of frustration, isolation, and exhaustion are during the first few months of an infant’s life.Usually, new parents can seek out social supports and external help—but what when we are all restricted in our movements and our contacts? What happens when families face child care challenges and more ongoing fallout from the COVID-19 pandemic? Here are a few suggestions:American Academy of Pediatricswww.aap.org and www.HealthyChildren.orgAdapted from the HealthyChildren.org article Tips for Coping with a New Baby During COVID-19 by Robert Sege, MD, PhD, FAAP (1/13/2022).The American Academy of Pediatrics (AAP) is an organization of 67,000 primary care pediatricians, pediatric medical subspecialists, and pediatric surgical specialists dedicated to the health, safety, and well-being of all infants, children, adolescents, and young adults.In all aspects of its publishing program (writing, review, and production), the AAP is committed to promoting principles of equity, diversity, and inclusion.The information contained in this publication should not be used as a substitute for the medical care and advice of your pediatrician. There may be variations in treatment that your pediatrician may recommend based on individual facts and circumstances.

A Large-Scale Benchmark Dataset for Anomaly Detection and Rare Event Classification for Audio Forensics

With the emergence of new digital technologies, a significant surge has been seen in the volume of multimedia data generated from various smart devices. Several challenges for data analysis have emerged to extract useful information from multimedia data. One such challenge is the early and accurate detection of anomalies in multimedia data. This study proposes an efficient technique for anomaly detection and classification of rare events in audio data. In this paper, we develop a vast audio dataset containing seven different rare events (anomalies) with 15 different background environmental settings (e.g., beach, restaurant, and train) to focus on both detection of anomalous audio and classification of rare sound (e.g., events—baby cry, gunshots, broken glasses, footsteps) events for audio forensics. The proposed approach uses the supreme feature extraction technique by extracting mel-frequency cepstral coefficients (MFCCs) features from the audio signals of the newly created dataset and selects the minimum number of best-performing features for optimum performance using principal component analysis (PCA). These features are input to state-of-the-art machine learning algorithms for performance analysis. We also apply machine learning algorithms to the state-of-the-art dataset and realize good results. Experimental results reveal that the proposed approach effectively detects all anomalies and superior performance to existing approaches in all environments and cases.

Neural Activation to Infant Cry Among Latina and Non-Latina White Mothers

Neural Activation to Infant Cry Among Latina and Non-Latina White Mothers

IOT Based Baby Monitoring System

Abstract: The current number of working mothers has increased dramatically. Later, child-rearing has become a daily challenge for many families. Thus, many parents send their children to grandparents' homes or to daycare centers. However, parents are not always able to monitor their children's every move. Therefore, the Internet of Things-based Baby Monitoring System (IoTBBMS) is being proposed as an efficient and inexpensive IoT-based monitoring system in real time. We also came up with a new algorithm for our system that plays a key role in providing better child care in the absence of parents. In the design process, the Node Micro-Controller Unit (NodeMCU) is used to collect sensor-readable data and upload it via Wi-Fi to the AdaFruit BLYNK server. The proposed system uses sensors to monitor important fetal parameters, such as ambient temperature, humidity, and crying. The system structure consists of a baby's crib that will automatically swipe using the engine when the baby cries. Parents can also monitor their children's condition with an external webcam and open a playful toy located on the baby's crib from a BLYNK server to entertain the baby. The proposed prototype of the system is designed and tested to prove its cost-effectiveness and simplicity and to ensure safe operation to enable child rearing anywhere and anytime via the network. Finally, the child monitoring system is proven to be effective in monitoring the child's condition and the environment in accordance with the model. All data taken from the sensors / modules will be stored in the Mobile application and periodically updated. The Health Algorithm is used in these databases to get information about useful physical conditions as any common symptoms of the disease can be easily identified. The proposed prototype of the system is designed and tested to prove its cost-effectiveness and simplicity and to ensure safe operation to enable child rearing anywhere and anytime via the network. Finally, the child monitoring system is proven to be effective in monitoring the child's condition and the environment according to the prototype. Keywords: IOT, Research, Node MCU, BLYNK, MQTT

Parent to Baby Emotions: Parental Immediate Emotional Reactions towards Infant Crying

Parent to Baby Emotions: Parental Immediate Emotional Reactions towards Infant Crying

Effectiveness of breastfeeding for pain relief in infants during vaccination at Bharatpur

Background: Vaccination is an integral part of childhood development since it protects children from a variety of diseases. It is, however, the most common cause of pain in children. Breastfeeding is effective tool for reducing pain in infants during vaccinations. The objective of the study was to determine the effectiveness of breastfeeding for pain relief in infants during vaccination.Methods: A true experimental pretest and posttest design was conducted at Maternal and Child Health Clinic at Bharatpur, Nepal among 140 infants receiving pentavalent vaccines (Diphtheria, Pertussis, Tetanus, Hepatitis B and Hemophilus influenzae B). A structured interview schedule was used to collect socio-demographic information of mothers and infants. Bio physiologic method was used to determine biological and physical status of the infants. Modified behavioral pain scale was used to measure the level of pain in infants in both the control and experimental groups.Results: The study revealed that the total modified behavioral pain scale (mean ±SD after vaccination was 8.74±0.53 in control and 8.23±1.07 in experimental respectively. The study showed that breastfeeding had significantly lowered pain level (p&lt;0.001) and lessened duration of cry (p=0.002) in infants in experimental group than in control group after vaccination. However, related to injectable vaccination, the study showed that breastfeeding did not significantly stabilized heart rate in infants in both the groups (p=0.122).Conclusions: The study concluded that the infants who were breastfed experienced less pain than those who were not breastfed during vaccination.

Longitudinal Study of Maternal Beliefs About Infant Crying During the Postpartum Period: Interplay With Infant's Temperament.

Infant crying is an important signal for their survival and development, and maternal beliefs about crying predict responsiveness to crying. Most studies have considered caregivers’ reactions to crying to be fixed, and it is unclear how they change with their caregiving experience. Additionally, it has recently been suggested that there is a bidirectional relationship between changes in mothers’ beliefs about crying and infants’ temperament. This study examined that relationship using a longitudinal study design. Maternal beliefs about crying and infant temperament of 339 Asian first-time mothers (mean age = 28.7 years, SD = 4.1) were measured at 1-month intervals over 4 months. There were 289 participants in Wave 2, 240 in Wave 3, and 164 in Wave 4. Prior to the main survey, we conducted a pre-survey to confirm the reliability and validity of the Japanese version of the Infant Crying Questionnaire. The results showed that parent-oriented beliefs, which focus on the caregiver rather than the crying infant, increased in mothers who had infants aged 3 months or older at Wave 1. We also found that the process of change in maternal beliefs was not uniform, and that infants high on surgency predicted changes in maternal beliefs about infant crying. Longitudinal studies of caregivers’ changes, such as the present study, are expected to contribute to understanding the co-development of caregivers and infants.

Convolutional Neural Networks for Audio-Based Continuous Infant Cry Monitoring at Home

Cry is an important signal in early infancy for parents to understand needs of their baby and thereby to provide timely parenting/soothing or to be reassured. Thanks to the recent advancement of signal processing, deep learning, and internet-of-things technologies, smart baby monitors with a microphone and/or a video camera have attracted a lot of attention to be used in a baby room to assist parental activities. In this paper, we propose a two-step approach to detect infant cries automatically with continuous audio signals. We first identify and remove the segments without clear sounds (background noise) using a volume-based thresholding algorithm, followed by convolutional neural network (CNN) models to further detect infant cries. The CNN operates on the log linear-scale filterbank energies of audio signals to extract features for cry detection. In this study, a large set of audio data (151.8 hours) collected from five infants in home settings were included. Our proposed approach achieved a mean accuracy of 98.6% in identifying background noise (with only 2 out of 3209 cry segments missed) and a mean accuracy of 92.2% in detecting cries from other non-background sounds.

Deep learning systems for automatic diagnosis of infant cry signals

Nowadays, deep learning architectures are promising artificial intelligence systems in various applications of biomedical engineering. For instance, they can be combined with signal processing techniques to build computer-aided diagnosis systems used to help physician making appropriate decision related to the diagnosis task. The goal of the current study is to design and validate various deep learning systems to improve diagnosis of infant cry records. Specifically, deep feedforward neural networks (DFFNN), long short-term memory (LSTM) neural networks, and convolutional neural networks (CNN) are designed, implemented and trained with cepstrum analysis-based coefficients as inputs to distinguish between healthy and unhealthy infant cry records. All deep learning systems are validated on expiration and inspiration sets separately. The number of convolutional layers and number of neurons in hidden layers are respectively varied in CNN and DFFNN. It is found that CNN achieved the highest accuracy and sensitivity, followed by DFFNN. The latter, obtained the highest specificity. Compared to similar work in the literature, it is concluded that deep learning systems trained with cepstrum analysis-based coefficients are powerful machines that can be employed for accurate diagnosis of infant cry records so as to distinguish between healthy and pathological signals.

Mothers’ implicit and explicit attitudes towards infant crying: predicting postpartum depressive symptoms

ABSTRACT The current study addressed whether mothers' explicitly expressed and implicitly held attitudes towards infant crying (n = 71) differ with each other and how these two types of attitudes relate to mothers' depressive symptoms during the transition to parenthood. Neither mothers' explicit nor implicit attitudes towards infant crying predicted mothers' postnatal depressive symptoms. Mothers' explicit infant-oriented attitudes (i.e. attachment and communication) were found to moderate the links between mothers' implicit attitudes of infant crying-positive attributes and mothers' postpartum depressive symptoms. The present study provides empirical support for the application of a dual process framework to understand how pregnant women perceive infants' crying. The findings suggest that mental health supports urgently needs be offered to mothers with depressive symptoms.