Infant Cry Research Articles

Interactions between poor sleep and negative social cognition pose risk for lower maternal sensitivity.

The extent to which mother and infant sleep predict maternal sensitivity as (a) main effects and (b) moderate the association between social cognition about infant crying (i.e., cry processing) and maternal sensitivity was examined in a sample of 299 mother-infant dyads (43% of mothers non-White; 50.5% of infants female). Infant- and mother-oriented cry processing were assessed prenatally using a video recall procedure and mothers self-reported demographics and characteristics reflecting emotional risk. When infants were 2 months old, mothers reported their depressive symptoms and mother and infant sleep. Maternal sensitivity and infant negative mood were observed during free play and the still face. There were no main effects of mother or infant sleep on maternal sensitivity over and above covariates. However, infant total sleep duration across night and day and mother sleep disturbance moderated the effect of mother-oriented cry processing on sensitivity. Specifically, mother-oriented cry processing was associated with lower maternal sensitivity only among mothers whose infants had lower sleep duration and who reported more sleep disturbances. Moderating effects were not apparent for infant night wakings or time awake or mothers' total sleep problems. Constrained opportunities for sleep or respite across the entire day and the totality of mothers' nighttime sleep disturbances, not just those specific to infants' night wakings, undermine maternal sensitivity by exacerbating mothers' preexisting tendency to have self-focused and negative reactions to infant distress. The efficacy of interventions designed to facilitate maternal sensitivity and infant adjustment may be enhanced by adding targeted foci on maternal and infant sleep. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prepartum and postpartum mothers' and fathers' feelings of frustration in response to infant crying.

In this cross-sectional study performed in Canada, we evaluated the frustration levels of prepartum and postpartum mother and father couple-pairs. Our goal was to determine if there were differences in frustration levels between mothers and fathers while listening to prolonged infant crying, and further, how frustration levels might differ between prepartum and postpartum samples. Using two discrete groups, prepartum (Sample 1; N=48) and postpartum (Sample 2; N=44) mother and father couple-pairs completed 600 s of listening to audio-recorded infant cry sounds. Participants continuously reported their subjective frustration using a computerized Continuous Visual Analog Scale (CVAS). There was no significant difference in frustration responses between mothers and fathers across both prepartum and postpartum samples. Postpartum mothers and fathers experienced greater frustration than their prepartum counterparts, and frustration increased faster in postpartum couples compared to prepartum couples. Informing first-time parents of the universal experiences of frustration to prolonged crying bouts that are characteristic of their infant's early weeks of life may lead to greater understanding towards their infant, and perhaps decreased instances of harmful responses.

Baby Crying Sound Classification using Convolutional Neural Network

Crying is a crucial means of communication for newborns, crying is a newborn's early form of communication. Many individuals are unable to recognise a baby's intention from cry unless they have the appropriate training or expertise, such as nurses, paediatricians, and childcare professionals. Accurately interpreting a baby's cry can be challenging. In this research paper, the study uses a method for classifying baby crying sounds using a Convolutional Neural Network (CNN) and the dataset includes belly pain, burping, discomfort, hungry, and tired for total of 3,495 one-second-long audio clips. The research methodology involves preprocessing the audio data, extracting Mel-Frequency Cepstral Coefficients (MFCC) as features, and training the CNN model. To determine the optimal architecture, two different configurations of the CNN model are evaluated. The settings for both configurations are the same, except for the layers. The first configuration utilizes 100, 200, and 100 neurons for the respective layers, while the second configuration employs 256, 512, and 256 neurons for each layer. the results have already been evaluated that the second configuration, with deeper and more complex layers, achieves higher accuracy (86%) compared to the first configuration (84%). The study demonstrates the effectiveness of CNNs in classifying baby cries and highlights the importance of model architecture in achieving accurate classification results. Future research could explore larger and more diverse datasets to improve generalizability.

Infant carrying: Associations with parental reflective functioning, parental bonding and parental responses to infant crying.

Infant carrying may have beneficial effects on the parent-infant relationship but only limited research has been conducted in this area. Therefore, the main aim of the current study was to investigate whether infant carrying is associated with parental reflective functioning, parental bonding, and parental (emotional) and behavioral responses to infant crying, key elements within the parent-infant relationship, promoting infant development. Parents reporting high levels (N=389) of infant carrying (six times a week or daily) and parents reporting low levels (N=128) of infant carrying (less than once a week or not at all) who participated in an online survey about the developing parent-infant relationship in Germany were included in the present study. Standardized questionnaires were used to assess parental reflective functioning, parental bonding impairments, and emotional responses to infant crying. Further insensitive (non-responsive and hostile) behaviors in response to infant crying were assessed. Parents with high levels of infant carrying showed better parental reflective functioning, lower parental bonding problems, less negative emotions, and less insensitive behaviors in response to infant crying.

No baby cries and silent neighbourhood: the effect of human capital investment on fertility

ABSTRACT The decline in fertility rates in developed countries poses significant challenges. One of the primary factors contributing to this decline is the increasing educational attainment among the general population, as individuals with higher levels of education tend to have fewer children. However, understanding the effect of human capital investment on fertility decline is difficult due to potential endogeneity issues. In this study, we use the BK21 programme, a Korean government scholarship programme for graduate students, as an instrument to estimate the effect of human capital investment on fertility. Using this instrument, we resolve potential endogeneity issues, provide the causal relationship between human capital investment and fertility decline, and offer insights for policymakers by highlighting the inflection point of economic growth through human capital accumulation.

Baby cry recognition based on WOA-VMD and an improved Dempster–Shafer evidence theory

Background and objectiveConflict may happen when more than one classifier is used to perform prediction or classification. The recognition model error leads to conflicting evidence. These conflicts can cause decision errors in a baby cry recognition and further decrease its recognition accuracy. Thus, the objective of this study is to propose a method that can effectively minimize the conflict among deep learning models and improve the accuracy of baby cry recognition. MethodsAn improved Dempster–Shafer evidence theory (DST) based on Wasserstein distance and Deng entropy was proposed to reduce the conflicts among the results by combining the credibility degree between evidence and the uncertainty degree of evidence. To validate the effectiveness of the proposed method, examples were analyzed, and applied in a baby cry recognition. The Whale optimization algorithm-Variational mode decomposition (WOA-VMD) was used to optimally decompose the baby cry signals. The deep features of decomposed components were extracted using the VGG16 model. Long Short-Term Memory (LSTM) models were used to classify baby cry signals. An improved DST decision method was used to obtain the decision fusion. ResultsThe proposed fusion method achieves an accuracy of 90.15% in classifying three types of baby cry. Improvement between 2.90% and 4.98% was obtained over the existing DST fusion methods. Recognition accuracy was improved by between 5.79% and 11.53% when compared to the latest methods used in baby cry recognition. ConclusionThe proposed method optimally decomposes baby cry signal, effectively reduces the conflict among the results of deep learning models and improves the accuracy of baby cry recognition.

Neural correlates of altered emotional responsivity to infant stimuli in mothers who use substances

Mothers who use substances during pregnancy and postpartum may have altered maternal behavior towards their infants, which can have negative consequences on infant social-emotional development. Since maternal substance use has been associated with difficulties in recognizing and responding to infant emotional expressions, investigating mothers' subjective responses to emotional infant stimuli may provide insight into the neural and psychological processes underlying these differences in maternal behavior. In this study, 39 mothers who used substances during the perinatal period and 42 mothers who did not underwent functional magnetic resonance imaging while viewing infant faces and hearing infant cries. Afterwards, they rated the emotional intensity they thought each infant felt (‘think’-rating), and how intensely they felt in response to each infant stimulus (‘feel’-rating). Mothers who used substances had lower ‘feel’-ratings of infant stimuli compared to mothers who did not. Brain regions implicated in affective processing (e.g., insula, inferior frontal gyrus) were less active in response to infant stimuli, and activity in these brain regions statistically predicted maternal substance-use status. Interestingly, ‘think’-ratings and activation in brain regions related to cognitive processing (e.g., medial prefrontal cortex) were comparable between the two groups of mothers. Taken together, these results suggest specific neural and psychological processes related to emotional responsivity to infant stimuli may reflect differences in maternal affective processing and may contribute to differences in maternal behavior in mothers who use substances compared to mothers who do not. The findings suggest potential neural targets for increasing maternal emotional responsivity and improving child outcomes.

Can You Understand Why I Am Crying? A Decision-making System for Classifying Infants’ Cry Languages Based on DeepSVM Model

Scientific and therapeutic advances in perinatology and neonatology have improved the survival prospects of preterm and extremely-low-birth-weight infants. Infants’ cries are a valuable noninvasive tool for monitoring their neurologic health, especially if they are premature. Automatic acoustic analysis and data mining are employed in this study to determine the discriminative features of preterm and full-term infant cries. The use of machine learning for recognizing sounds in a newborn's cry language has received less attention than previous methods for analyzing the sounds. Moreover, to extract appropriate features from infant cries, adequate knowledge and appropriate signal descriptors are required. Accordingly, to analyze infant cry language, we propose an approach that uses fractal descriptors to extract discriminant features from spectrograms of windowed signals, followed by iterative neighborhood component analysis (iNCA) to select appropriate features. Additionally, the improved deep support vector machine (DeepSVM) is used to classify the infants’ crying types and their meanings. The proposed method is verified using a newborn sound dataset. According to the classification of five types of crying perception based on various characteristics, 98.34% of all crying perceptions have been recognized. Although there are many classes examined, the feature extraction method based on the fractal method and our optimal classification have a much higher diagnostic accuracy compared with similar methods for analyzing baby crying language. The proposed method can overcome many problems associated with analyzing babies’ crying sounds and understanding their language, such as uncertainty and unusual errors in classification.

Primal Screams: The Infantile Cry in Simone Weil

The main thesis of this essay is that non-linguistic infantile cries towards the nondefinable constitute, for Simone Weil, the essence of the human. The author begins by surveying, for the first time, Weil’s depiction of the infant’s cry as a scream of an infinite desire towards nothing definite. In the second part, in which the author analyzes the infantile cry introduced in Weil’s later writings this desire, it will be presented as fundamental to being. The infantile cry expresses mutely a desire for the indescribable good. Since it is cried from birth till death, its designation as infantile is revealed to indicate not one’s age but one’s nonlinguistic essence. While recent scholarship emphasized the importance of silence in Weil’s thought, no attention had been given to the significance of the ineffable to her philosophy. By studying the infant and infantile cry, this essay will show how the inarticulate desire towards the unattainable comprises the truth of being.

A Comparative Study of Machine Learning Methods for Baby Cry Detection Using MFCC Features

The vocalization of infants, commonly known as baby crying, represents one of the primary means by which infants effectively communicate their needs and emotional states to adults. While the act of crying can yield crucial insights into the well-being and comfort of a baby, there exists a dearth of research specifically investigating the influence of the audio range within a baby cry on research outcomes. The core problem of research is the lack of research on the influence of audio range on baby cry classification on machine learning. The purpose of this study is to ascertain the impact of the duration of an infant’s cry on the outcomes of machine learning classification and to gain knowledge regarding the accuracy of results F1 score obtained through the utilization of the machine learning method. The contribution is to enrich an understanding of the application of classification and feature selection in audio datasets, particulary in the context of baby cry audio. The utilized dataset, known as donate-a-cry-corpus, encompasses five distinct data classes and possesses a duration of seven seconds. The employed methodology consists of the spectrogram technique, cross-validation for data partitioning, MFCC feature extraction with 10, 20, and 30 coefficients, as well as machine learning models including Support Vector Machine, Random Forest, and Naïve Bayes. The findings of this study reveal that the Random Forest model achieved an accuracy of 0.844 and an F1 score of 0.773 when 10 MFCC coefficients were utilized and the optimal audio range was set at six seconds. Furthermore, the Support Vector Machine model with an RBF kernel yielded an accuracy of 0.836 and an F1 score of 0.761, while the Naïve Bayes model achieved an accuracy 0.538 and F1 score of 0.539. Notably, no discernible differences were observed when evaluating the Support Vector Machine and Naïve Bayes methods across the 1-7 second time trial. The implication of this research is to establish a foundation for the advancement of premature illness identification techniques grounded in the vocalizations of infants, thereby facilitating swifter diagnostic processes for pediatric practitioners.

Multi-task Learning for Audio-based Infant Cry Detection and Reasoning.

Infant cry is a crucial indicator that offers valuable insights into their physical and mental conditions, such as hunger and pain. However, the scarcity of infant cry datasets hinders the model's generalization in real-life scenarios. The varying voiceprint characteristics among infants further exacerbate this challenge, deteriorating the model's performance on unseen infants. To this end, we propose a multi-task model for Infant Cry Detection and Reasoning (ICDR). It leverages datasets from two tasks to enrich data diversity and introduces an efficient attention module to achieve inter-task feature supplementarity. To mitigate the impact of subject differences, ICDR introduces an intra-task contrastive mixture of experts (CMoE) module that adaptively allocates experts to reduce subject variance and applies contrastive learning to enhance the representation consistency of samples from different infants in the same state. Extensive cross-subject experiments show that ICDR outperforms the state-of-the-art models in infant cry detection and reasoning, with an improvement of 2-9% in the F1-score. This demonstrates that multi-task learning effectively enhances the model's generalization ability by inter-task attention and intra-task CMoE.

Evaluating Noise-Robustness of Convolutional and Recurrent Neural Networks for Baby Cry Recognition

Evaluating Noise-Robustness of Convolutional and Recurrent Neural Networks for Baby Cry Recognition

CQT-Based Cepstral Features for Classification of Normal vs. Pathological Infant Cry

CQT-Based Cepstral Features for Classification of Normal <i>vs.</i> Pathological Infant Cry

Comparison of Effectiveness of Manual Therapy for Infant Crying: systematic review and meta-analysis of randomized controlled trials.

The aim of this systematic review and meta-analysis is to assess and compare the effectiveness of manual therapy in alleviating infant crying, a common symptom of nocturnal crying (NC) and infantile colic (IC). Total effective rate, crying time and adverse events were used as outcome indicators. To assess the quality, the risk of bias was determined for each study by two authors, using the Cochrane Collaboration's risk of bias tool. RevMan 5.0 was used for data analysis. A total of 98 articles were identified from 6 electronic databases. Among them, twenty-seven studies which included 13 NC and 14 IC were included. Meta-analysis showed favorable effects tuina therapy on total effective rate (TER) of NC (RR 1.20 [95% CI 1.05 to 1.37], p = 0.007), chiropractic therapy on crying time change of IC (SMD -0.83 [95% CI -1.61 to -0.06], p = 0.04) and massage on total crying time of IC (SMD -0.86 [95% CI -1.09 to -0.63], p < 0.00001). This systematic review compares different manual therapies for the treatment of NC and IC. While tuina, chiropractic, and massage show results in alleviating symptoms, the overall evidence remains limited due to the low quality and heterogeneity of the included studies. Therefore, further high-quality research with unified control groups is needed to establish manual therapy as a recommended treatment option for NC and IC. Protocol registration number is CRD42022348143 01/08/2022.

A Ranked-Aware GA with HoG Features for Infant Cry Classification

A Ranked-Aware GA with HoG Features for Infant Cry Classification

Emotion dysregulation and reward responsiveness as predictors of autonomic reactivity to an infant cry task among substance-using pregnant and postpartum women.

Maternal substance use may interfere with optimal parenting, lowering maternal responsiveness during interactions with their children. Previous work has identified maternal autonomic nervous system (ANS) reactivity to parenting-relevant stressors as a promising indicator of real-world parenting behaviors. However, less is known about the extent to which individual differences in emotion dysregulation and reward processing, two mechanisms of substance use, relate to maternal ANS reactivity in substance-using populations. The current study examined associations among emotion dysregulation, reward responsiveness, and ANS reactivity to an infant cry task among 77 low-income and substance-using women who were either pregnant (n=63) or postpartum (n=14). Two indicators of ANS functioning were collected during a 9min computerized infant cry task (Crybaby task): respiratory sinus arrhythmia (RSA) and pre-ejection period. Mothers also completed self-reported measures of emotion dysregulation and reward responsiveness. Analyses revealed that trait emotion regulation was associated with RSA reactivity to the Crybaby task, such that greater emotion dysregulation was associated with greater RSA reduction during the infant cry task than lower emotion dysregulation. Reward responsiveness was not significantly associated with either indicator of ANS reactivity to the task. Findings revealed distinct patterns of associations linking emotion dysregulation with ANS reactivity during a parenting-related computerized task, suggesting that emotion regulation may be a key intervention target for substance-using mothers.

Vibe - With people with hearing impairment

Hearing loss significantly impacts daily life, leading to communication difficulties, social isolation, and increased risk of accidents. This can cause frustration, anxiety, and depression. The use of sound indication devices improve the quality of life for individuals with hearing impairment by providing them with a means to detect sounds in their environment. We have developed Vibe, a device that alerts a user to specific sounds (a baby's cry, an alarm, a door knock, a vehicle horn, or a spoken name) in their environment. Our device uses EdgeML, a form of machine learning, to perform tasks locally on the device rather than relying on cloud-based services. This approach has the advantage of reduced latency and improved privacy. The EdgeML model was trained on a locally collected dataset, for better applicability to the Indian environment. Devices such as Vibe will help individuals with hearing impairment navigate their surroundings safely and confidently, leading to increased independence and improved mental health.

Pregnant women's autonomic responses to an infant cry predict young infants' behavioral avoidance during the still-face paradigm.

Research suggests that women's autonomic nervous system responses to infant cries capture processes that affect their parenting behaviors. The aim of this study was to build on prior work by testing whether pregnant women's autonomic responses to an unfamiliar infant crying also predict their infants' emerging regulation abilities. Participants included 97 women in their third trimester of pregnancy, located in the United States. Most participants identified as White/non-Hispanic (48%) or Hispanic (30%), their mean age was approximately 30 years, and the modal family income was $40,000-$79,999. Pregnant women's respiratory sinus arrhythmia (RSA) and skin conductance levels (SCL)-which are thought to capture emotional engagement and behavioral inhibition, respectively-were measured while the women watched a relaxing video and a video of an unfamiliar infant crying. Approximately 7 months later, women and their infants completed the still-face paradigm (SFP). Infants' avoidance and resistance behaviors during the SFP reunions were rated. Pregnant women's RSA and SCL responses to the infant cry video uniquely predicted infants' avoidance (but not resistance) during the SFP. Infants displayed higher levels of avoidance when their mothers exhibited lower levels of RSA reactivity or when their mothers exhibited higher levels of SCL activity in response to the infant cry video. Maternal sensitivity during mother-infant free-play interactions did not mediate the associations between pregnant women's autonomic responses to the cry video and infants' avoidant behavior during the SFP. Discussion focuses on potential mechanisms underlying associations between pregnant women's autonomic responses to infant distress and infants' socioemotional development. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Baby cry recognition based on SLGAN model data generation and deep feature fusion

Deep learning models have been applied in baby cry recognition to enhance the recognition accuracy. However, the current research still suffers from data imbalance problem, which leads to bias in model learning. Sparse Autoencoder Long Short-Term Memory based Generative Adversarial Network (SLGAN) is proposed to solve the data imbalance problem. The proposed SLGAN model generates new baby cry data to ensure the number of samples for every cry class is equal. Speech features are extracted using Mel-spectrograms and Short-Time Fourier Transform (STFT). Two deep learning models, i.e. VGG16 and VGG19 are used to extract the deep features. The deep features are then dimensionally reduced by using Principal Component Analysis (PCA). A sparse autoencoder model is used to fuse the deep features. Finally, the fused features are trained and classified using the Deep Neural Network. The experimental results show that the proposed method outperforms the existing methods.

Childminder knowledge of shaken baby syndrome and the role played by childminders in prevention: An observational study in France

BackgroundShaken baby syndrome (SBS) triggers negative short- and long-term outcomes. In France, registered childminders are the principal source of daycare. They may encounter SBS imparted by caregivers or simply excessive infant crying. The aim of the study was to explore childminder knowledge on SBS, the source of information, the responses to infant crying, how childminders perceived their roles in terms of caregiver SBS prevention, and the factors associated with a good knowledge of SBS. MethodsThe participants were registered childminders working in the French department of Gironde who had email accounts. This observational study employed an anonymous online questionnaire distributed by the maternal and child health services unit of the Gironde department over 5 weeks from 16 September 2021. Data on childminder characteristics, knowledge on SBS, responses to infant crying, and perceptions of their roles in SBS prevention were collected. The knowledge score ranged from 0 (all wrong answers) to 30 (all correct answers). ResultsA total of 779 registered childminders participated; 43.9 % had learnt about SBS during their initial training and 75 % before (other training) or after initial training. The median knowledge score was 19/30 (interquartile range [16; 21]). A higher educational level, previous other professional experience, training on SBS, and responsibility for few children were associated with higher scores. Ten reported that they did not advise caregivers who complained of infant crying. ConclusionChildminders require training on SBS and SBS prevention.