Infant Cry Classification Research Articles

Classification of Infant Crying Sounds Using SE-ResNet-Transformer

Recently, emotion analysis has played an important role in the field of artificial intelligence, particularly in the study of speech emotion analysis, which can help understand one of the most direct ways of human emotional communication—speech. This study focuses on the emotion analysis of infant crying. Within cries lies a variety of information, including hunger, pain, and discomfort. This paper proposes an improved classification model using ResNet and transformer. It utilizes modified Mel-frequency cepstral coefficient Mel-frequency cepstral coefficient (MFCC) features obtained through feature engineering from infant cries and integrates SE attention mechanism modules into residual blocks to enhance the model’s ability to adjust channel weights. The proposed method achieved 93% accuracy rate in experiments, offering advantages of shorter training time and higher accuracy compared to other traditional models. It provides an efficient and stable solution for infant cry classification.

Premature Infant Cry Classification via Elephant Herding Optimized Convolutional Gated Recurrent Neural Network

Premature Infant Cry Classification via Elephant Herding Optimized Convolutional Gated Recurrent Neural Network

Infant cry classification using an efficient graph structure and attention-based model

Crying serves as the primary means through which infants communicate, presenting a significant challenge for new parents in understanding its underlying causes. This study aims to classify infant cries to ascertain the reasons behind their distress. In this paper, an efficient graph structure based on multi-dimensional hybrid features is proposed. Firstly, infant cries are processed to extract various speech features, such as spectrogram, mel-scaled spectrogram, MFCC, and others. These speech features are then combined across multiple dimensions to better utilize the information in the cries. Additionally, in order to better classify the efficient graph structure, a local-to-global convolutional neural network (AlgNet) based on convolutional neural networks and attention mechanisms is proposed. The experimental results demonstrate that the use of the efficient graph structure improved the accuracy by an average of 8.01% compared to using standalone speech features, and the AlgNet model achieved an average accuracy improvement of 5.62% compared to traditional deep learning models. Experiments were conducted using the Dunstan baby language, Donate a cry, and baby cry datasets with accuracy rates of 87.78%, 93.83%, and 93.14% respectively.

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

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

Bbcry, a lightweight deep learning model for infant cry classification

Babies who can’t communicate through language use crying as a way to express themselves. By identifying the unique characteristics of their cries, parents can quickly meet their needs and ensure their health. This study aimed to create a lightweight deep learning model called Bbcry to classify the cries of babies and determine their needs, such as hunger, pain, normal, deafness, or asphyxia. The model was trained using the Chillanto dataset and underwent three stages of development. Initially, the Wav2Vec 2.0 model was utilized as a teacher for the Knowledge Distillation (KD) method and applied to the transformer and prediction layers to reduce the number of required parameters. Then, a projection head layer was added and linked to the transformer layers to control their impact on the Wav2Vec 2.0 model. This resulted in the first version of the Bbcry model with an accuracy of 93.39% and an F1-score of 87.60%. Finally, the number of transformer layers was reduced to create the Bbcry-v4 model with only 9.23 million parameters, which used only 10% of the parameters of Wav2Vec 2.0 while only slightly reducing accuracy and F1-score. The study concludes with a software demonstration that shows the proposed model’s ability to accurately recognize and determine the needs of infants based on their cries.

Premature Infant Cry Classification via Deep Convolutional Recurrent Neural Network Based on Multi-class Features

Premature Infant Cry Classification via Deep Convolutional Recurrent Neural Network Based on Multi-class Features

Infant cry classification by MFCC feature extraction with MLP and CNN structures

In this study, Dunstan’s infant cry data set is pre-processed with the feature vector approach, including MFCC (19 features) and energy (one feature). By using extracted features and Support Vector Machine (SVM), MultilayerPerceptron (MLP), and Convolutional Neural Network (CNN) classifiers, five classes of infant cry (“Neh” = hungry; “Eh” = need to burp; “Owh” = tired; “Eairh” = stomach cramp; “Heh” = physical discomfort) are distinguished. The proposed MLP and CNN structures are analyzed according to the loss and the accuracy based on the epoch; moreover, to evaluate the performance of classifiers AUC-ROC, Confusion matrix, accuracy, f1_score, recall, and precision have been used. All three classifiers are analyzed, and their results show that the CNN-designed model has the best performance. Results show that the performance will improve by increasing the complexity of the model. With this approach, classifiers are run 10 times, and the average accuracy for SVM for SMOTE and non-SMOTE data are obtained with tolerance 0.823 ± 0.02, 0.861 ± 0.02, respectively. These accuracies for MLP are 0.876 ± 0.01, 0.892 ± 0.01, and finally, for CNN, are 0.921 ± 0.005, 0.911 ± 0.005. At the best condition, an accuracy of 92.1 % is obtained for five classes of infant cries by the proposed CNN structure.

Infant Posture Analyzer and Wet Diaper System

Abstract: Monitoring the well-being of infants is of utmost importance for ensuring their safety and providing timely care. This paper presents a novel approach to enhance traditional baby monitors by integrating intelligent classification of infant cries and real-time monitoring of sleep positions. The proposed smart baby monitor employs advanced signal processing techniques, machine learning algorithms, and computer vision methods to analyse audio and visual cues, providing valuable insights into a baby's needs and sleep patterns. This research paper presents the development of a Smart Baby Monitoring System equipped with one machine learning module and a wet diaper detection model. The system aims to enhance infant safety and improve caregiving practices. The first module employs YOLO and a Convolutional Neural Network (CNN) for position detection, identifying whether the baby is on their belly, back, or side. The position detection model achieves an average accuracy of 90%, with precision and recall rates 85%, ensuring reliable identification of the baby's needs Second module is a wet diaper detection module is incorporated to detect if the baby's diaper needs changing. The integration of these machine learning modules in the Smart Baby Monitoring System offers realtime monitoring, timely alerts, and improved infant care.

Infant cry classification by using different deep neural network models and hand-crafted features

Crying is the way babies communicate with the outside world. These cries may be related to the needs of the baby or maybe an expression of a medical disorder. For this reason, infant cries are examined to support inexperienced parents and to make an early diagnosis if there is a medical disorder. Infant cry signals are classified using signal processing methods such as hand-crafted features or image processing methods based on the spectral image of the cry. In this study, we investigate the effect of using hand-crafted features and spectral images individually and hybrid in the classification of infant cries. In this context, experiments were conducted with the 1D CNN model, transfer learning, texture analysis methods, hand-crafted features, and their combination. In addition, the number of classes used in most of the studies in the literature is two or three, whereas in this study 5-classes in the dataset are used. Classification with hand-crafted and hybrid features was performed with SVM, RNN, and PNN. In addition, hand-crafted features were also classified with 1D CNN. GoogLeNet, ShuffleNet, ResNet-18 were used for transfer learning in image-based classification. The results show that texture analysis methods are insufficient and that hand-crafted feature sets and spectrogram and scalogram images provide high success. In addition, the 1D CNN model showed lower success than traditional classifiers and transfer learning models. Especially on DB2, the lowest result was obtained with 1D CNN in all experiments. The results were compared on two data commonly used in the literature, and a success rate of 97.6% and 95.2% was achieved. These results show that both signal processing methods and spectrogram and scalogram images can be used successfully in infant cry classification studies.

Classification of asphyxia infant cry using hybrid speech features and deep learning models

Single speech feature such as Mel-Frequency Cepstral Coefficient (MFCC) has been used in most of the studies to classify asphyxia cry among infants. Other speech features such as Chromagram, Mel-scaled Spectrogram, Spectral Contrast and Tonnetz have not been reported in any study related to the classification of asphyxia cry. The study investigated the use of hybrid features of MFCC, Chromagram, Mel-scaled Spectrogram, Spectral Contrast and Tonnetz and deep learning models in classifying asphyxia cry. Deep learning models such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN) were used to classify infant cry between normal/non-asphyxia and asphyxia. The performance of the deep learning models was compared using concatenated hybrid features and single feature of MFCC. The Baby Chillanto Database was used in this study. CNN model performed better than DNN models when MFCC was used. DNN models performed better with hybrid features compared to that with single feature of MFCC. DNN with multiple hidden layers achieved an accuracy of 100% in classifying normal and asphyxia cry, and 99.96% for non-asphyxia and asphyxia cry when the hybrid features were used.

The Study of Learning System for Infant Cry Classification Using Discrete Wavelet Transform and Extreme Machine Learning

The learning system of infant cry is presented. This system consists of characteristics attraction technique and classification technique. The characteristics attraction of infant cry are based on Discrete Wavelet Transform (DWT) methods. Whilst the sound classification of coefficients characteristics uses Single Layer Neural Feed Forward (SLNF) as an Extreme Learning Machine (ELM). The Dunstan Baby Language (DBL) is the sound database for the proposed system. The sound database was collected from infants between birth and 6 months of age. Where the baby language groups are categorized into 5 types: "Eh", "Eairh", "Neh", "Heh" and "Owh", respectively. The accuracy of sound classification was designated at the number of hidden nodes of 10 – 50 with a training and testing ratio of 70/30. The suitable results are based on the number of epochs, accuracy and performances. The results show that the average accuracy of all discrete wavelet functions on the baby language are over 80%. The average performance of Sym2 is suitable for all baby language groups. Moreover, the average number of epochs of Bior3.1 is suitable for all baby language groups.

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%.

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.

Research on LSTM+Attention Model of Infant Cry Classification

According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher.

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models.

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

Biomedical Diagnosis of Infant Cry Signal Based on Analysis of Cepstrum by Deep Feedforward Artificial Neural Networks

The automatic analysis and detection of audio signals is an important field of research with promising applications in various biomedical engineering problems such as speech, heart murmur, and lung sound analysis and classification. In this regard, automatic classification of infant vocalizations is becoming an appealing research area for medical diagnosis in clinical milieu. Indeed, the analysis and classification of infant cry records is a conventional non-inva-sive technique to distinguish between healthy and unhealthy infants.

Time-frequency analysis-based method for application of infant cry classification

Automatic infant cry classification is one of the significant studies under medical engineering, adopting the medical and engineering techniques for the classification of diverse physical and physiological states of the infants. This paper proposes a new investigation of time-frequency (t-f)-based signal processing technique using wavelet packet spectrum (wpspectrum) for classification of newborn cry signals. The study was initialised with the extraction of a cluster of t-f features from the generated t-f matrix of recorded cry signals using wpspectrum by extending time-domain and frequency-domain features to the joint t-f domain. In accordance, conventional features such as mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs) were also extracted in order to compare the performance of the suggested t-f approach. Probabilistic neural network (PNN) and general regression neural network (GRNN) were used in classification. The proposed methodology was implemented to classify different sets of infant cry signals and the best empirical result of above 99% was reported.

Time-frequency analysis-based method for application of infant cry classification

Time-frequency analysis-based method for application of infant cry classification

A Low-Cost Intelligent Hardware System for Real-Time Infant Cry Detection and Classification

Cry of an infant serves as the main communication language to seek the attention of their caretakers. Acoustic characteristics of cries provide insights into the physiological and psychological states of the infants. To understand the reason behind the cries, caretakers pay attention to acoustic characteristics like tone, pitch, and loudness, etc. Infant cry classification has gained importance in both commercial and medical fields due to its applications such as baby monitoring and non-invasive diagnosis of health conditions of newborns in early stages. This paper discusses the implementation of a lowcost hardware device for real-time cry classification. Further, this presents the use of time and frequency domain features to detect cry words and identify the reasons. The proposed solution covers the use of classification techniques like artificial neural networks and k nearest neighbors to gain accuracy figures over 90% and 70% for cry detection and classification respectively while maintaining the resource utilization at a minimum level to keep hardware solution simple and low cost.

A Low-Cost Intelligent Hardware System for Real-Time Infant Cry Detection and Classification

Cry of an infant serves as the main communication language to seek the attention of their caretakers. Acoustic characteristics of cries provide insights into the physiological and psychological states of the infants. To understand the reason behind the cries, caretakers pay attention to acoustic characteristics like tone, pitch, and loudness, etc. Infant cry classification has gained importance in both commercial and medical fields due to its applications such as baby monitoring and non-invasive diagnosis of health conditions of newborns in early stages. This paper discusses the implementation of a lowcost hardware device for real-time cry classification. Further, this presents the use of time and frequency domain features to detect cry words and identify the reasons. The proposed solution covers the use of classification techniques like artificial neural networks and k nearest neighbors to gain accuracy figures over 90% and 70% for cry detection and classification respectively while maintaining the resource utilization at a minimum level to keep hardware solution simple and low cost.