Comparison of Supervised-learning Models for Infant Cry Classification / Vergleich von Klassifikationsmodellen zur Säuglingsschreianalyse

Fuhr, Tanja; Reetz, Henning; Wegener, Carla

doi:10.1515/ijhp-2015-0005

Cited by 14 publications

(3 citation statements)

References 72 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To determine the classification ability of the different models, Fuhr et al experimented differentiating healthy infant cries and cries of infants suffering from several diseases using 12 classifiers including SVM, decision tree, KNN, MLP, etc. The result showed only C5 decision tree and KNN achieved greater than 90% accuracy [90]. Applying many algorithms on the task before selecting the algorithm to use is impractical.…”

Section: Infant Cry Classification Models 411 Traditional Machine Lmentioning

confidence: 99%

A review of infant cry analysis and classification

Mudiyanselage

Gao

et al. 2021

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

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.

show abstract

Section: Infant Cry Classification Models 411 Traditional Machine Lmentioning

confidence: 99%

A review of infant cry analysis and classification

Mudiyanselage

Gao

et al. 2021

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

show abstract

“…Infant cry analysis is an interdisciplinary field of research involving physiology, anatomy, and phonetics. The findings of infant cry classification studies are significant for many healthcare professions, such as nurses, midwives, and speech and language therapists, as well as medical professions such as pediatricians, by assisting in the interpretation of the newborn cry to recognize an infant's needs or health state [2].…”

Section: Introductionmentioning

confidence: 99%

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

2023

IJIES

View full text Add to dashboard Cite

Infants typically cry to get their parents' attention. Through their cries, infants express their basic needs like hunger, tiredness, pain, and discomfort. Unfortunately, it is difficult to interpret cries to comprehend the demands of an infant. The only way to solve this problem is to analyze the infant's acoustic speech pattern and determine the cause of the crying. In this study, the cry signal is converted to a spectrogram image to take advantage of the wide spectral range of image-based features. Before generating the represented features, the watershed segmentation algorithm is used to remove distracting areas of the image. Then, histogram of gradients (HoG) features are generated. Because the feature vector has high dimensionality, two stages of dimensionality reduction are presented. First, the feature pool is decreased using the fisher score feature selection approach. The ideal feature set is then chosen using a combination of transfer learning, genetic algorithm (GA), and neural networks. To motivate GA to pick characteristics that will operate successfully with the neural network, a ranked aware mutation operator is suggested. As system evaluation material, the donateacry-corpus public dataset is employed. Experiments reveal that when 80 HoG features are generated and the best 37 Fisher scores are chosen, the model has the best accuracy of 92% when applying transfer learning to 11 hidden layers of the neural network. The study's findings support the use of image-based features to identify the cause of a baby's crying.

show abstract

“…In the next step of NCDSs, many different classification approaches have been explored. Support Vector Machine (SVM) [ 33 , 34 ], Probabilistic Neural Network (PNN) [ 24 ], Forest [ 35 ], Decision Trees [ 29 ], K-nearest Neighborhood (KNN) [ 36 ], and discriminant analysis are some of the algorithms implemented in this field [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems

Khalilzad

Kheddache

Tadj

2022

Entropy

View full text Add to dashboard Cite

The acoustic characteristics of cries are an exhibition of an infant’s health condition and these characteristics have been acknowledged as indicators for various pathologies. This study focused on the detection of infants suffering from sepsis by developing a simplified design using acoustic features and conventional classifiers. The features for the proposed framework were Mel-frequency Cepstral Coefficients (MFCC), Spectral Entropy Cepstral Coefficients (SENCC) and Spectral Centroid Cepstral Coefficients (SCCC), which were classified through K-nearest Neighborhood (KNN) and Support Vector Machine (SVM) classification methods. The performance of the different combinations of the feature sets was also evaluated based on several measures such as accuracy, F1-score and Matthews Correlation Coefficient (MCC). Bayesian Hyperparameter Optimization (BHPO) was employed to tailor the classifiers uniquely to fit each experiment. The proposed methodology was tested on two datasets of expiratory cries (EXP) and voiced inspiratory cries (INSV). The highest accuracy and F-score were 89.99% and 89.70%, respectively. This framework also implemented a novel feature selection method based on Fuzzy Entropy (FE) as a final experiment. By employing FE, the number of features was reduced by more than 40%, whereas the evaluation measures were not hindered for the EXP dataset and were even enhanced for the INSV dataset. Therefore, it was deduced through these experiments that an entropy-based framework is successful for identifying sepsis in neonates and has the advantage of achieving high performance with conventional machine learning (ML) approaches, which makes it a reliable means for the early diagnosis of sepsis in deprived areas of the world.

show abstract

Comparison of Supervised-learning Models for Infant Cry Classification / Vergleich von Klassifikationsmodellen zur Säuglingsschreianalyse

Cited by 14 publications

References 72 publications

A review of infant cry analysis and classification

A review of infant cry analysis and classification

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

An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems

Contact Info

Product

Resources

About