Newborn cry nonlinear features extraction and classification

Peralta-Malváez, Lizbeth; López-Rincón, Omar; Rojas-Velázquez, David; Valencia-Rosado, Luis Oswaldo; Rosas-Romero, Roberto; Etcheverry, Gibran

doi:10.3233/jifs-169510

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Mobile network animation images are prone to flicker, shadow and other phenomena [1]. In order to overcome these phenomena, reflect the main content of video and enhance the continuous expression of animation, it is necessary to extract stable and reliable features in the images.…”

Section: Introductionmentioning

confidence: 99%

Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

Yin

2022

Mobile Netw Appl

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In order to effectively extract the feature points of mobile network animation images and accurately reflect the main content of the video, an optimization method to extract the feature points of key frame images of mobile network animation is proposed. Firstly, the key frames are selected according to the content change degree of the animation video. The scale invariant feature transformation algorithm is used to describe the feature points of the key frame image of the animation video. The local feature points of the image are estimated by the constraint optimization method to realize the optimization extraction of the feature points of the key frame image of the mobile network animation. The efficiency of feature points extraction is analyzed from the number and effectiveness of feature points extraction, time-consuming and similarity invariance of feature points. The experimental results show that the proposed method has excellent adaptability, and can effectively extract feature points of mobile network animation image.

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Section: Introductionmentioning

confidence: 99%

Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

Yin

2022

Mobile Netw Appl

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“…Support vector machine (SVM) and expectation-maximization (EM) algorithms over an expert system were employed to classify the data. It shows that non-linear feature with an expert system-based classification approach gives better performance ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Ashwini

Vincent

Srinivasan

et al. 2021

Front. Public Health

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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.

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Effective infant cry signal analysis and reasoning using IARO based leaky Bi-LSTM model

Mala,

Darandale

2024

Computer Speech & Language

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Newborn cry nonlinear features extraction and classification

Cited by 3 publications

References 16 publications

Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Effective infant cry signal analysis and reasoning using IARO based leaky Bi-LSTM model

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