2021
DOI: 10.3389/fpubh.2021.670352
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Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Abstract: 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, a… Show more

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Cited by 27 publications
(8 citation statements)
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“…We utilized the following machine learning methods to develop prediction models for POD in patients with VHD, respectively using full variables and selected variables as input features: Random Forest Classifier [ 37 ], Logistic Regression [ 38 , 39 ], SVC [ 40 ], K-nearest Neighbors Classifier [ 41 ], Gaussian Naive Bayes [ 42 ], Gradient Boosting Decision Tree [ 43 ] and Perceptron [ 44 ]. These algorithmic models were then validated in the validation group to assess their performance.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…We utilized the following machine learning methods to develop prediction models for POD in patients with VHD, respectively using full variables and selected variables as input features: Random Forest Classifier [ 37 ], Logistic Regression [ 38 , 39 ], SVC [ 40 ], K-nearest Neighbors Classifier [ 41 ], Gaussian Naive Bayes [ 42 ], Gradient Boosting Decision Tree [ 43 ] and Perceptron [ 44 ]. These algorithmic models were then validated in the validation group to assess their performance.…”
Section: Resultsmentioning
confidence: 99%
“…We applied several supervised machine learning methods to both the full and selected feature sets to construct predictive models of delirium, Specifically, we utilized classical machine learning algorithms commonly employed for classification problems, including Random Forest Classifier [ 37 ], Logistic Regression [ 38 , 39 ], Support Vector Machine Classifier (SVC) [ 40 ], K-nearest Neighbors Classifier [ 41 ], Gaussian Naive Bayes [ 42 ], Gradient Boosting Decision Tree [ 43 ] and Perceptron [ 44 ]. The selection of these algorithms was determined by factors such as the sample size and number of features.…”
Section: Methodsmentioning
confidence: 99%
“…Support vector regression (SVR) model uses hyperplane as the decision boundary to find the optimal support vector and to build the segmentation plane. In order to realize linearly separation, kernel function is applied to map the linear inseparable data to a higher-level high-dimensional feature space 35 . The final decision only needs a small number of support vectors, and is not restricted by the number of samples.…”
Section: Methodsmentioning
confidence: 99%
“…Multi-class classification on infant cry data was performed by Vincent et al ( 2021 ) with pain, hunger, and sleepiness classes. The audio signal was primarily converted to a spectrum image using an STFT method.…”
Section: Related Workmentioning
confidence: 99%