Classification of Infant Cries with Asphyxia Using Multilayer Perceptron Neural Network

Zabidi, Azlee; Khuan, Lee Yoot; Mansor, W.; Yassin, Ihsan Mohd; Sahak, Rohilah

doi:10.1109/iccea.2010.47

Cited by 29 publications

(8 citation statements)

References 8 publications

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“…It is a cepstral representation of the audio signals. Researchers use it to test proposed approaches [17,29,49,52,57,[60][61][62] and often use it for baseline experiments [13,15,22,31,37,63]. Liu et al used MFCC along with two other cepstral features Linear Prediction Cepstral Coefficients (LPCC) and Bark Frequency Cepstral Coefficients (BFCC) for infant cry reason classification.…”

Section: Cepstral Domain Featuresmentioning

confidence: 99%

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A review of infant cry analysis and classification

Mudiyanselage

Gao

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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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.

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Section: Cepstral Domain Featuresmentioning

confidence: 99%

“…F-ratio method was used to select the top 20 MFCC features. The coefficients that have significant importance have higher F-ratio scores [63]. In 2013, Yamamoto et al used Principal Component Analysis (PCA) to reduce the dimensionality of FFT features [23].…”

Section: Feature Selectionmentioning

confidence: 99%

A review of infant cry analysis and classification

Mudiyanselage

Gao

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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“…An automatic baby cry detector has many applications: it is commonly employed in safety-related devices, such as baby monitors [32], and has been proposed as part of a system to detect children forgotten in vehicles [33]; some commercial products featuring cry detection technology include [34][35][36]. Identification, followed by classification of the cry signals, can be useful for medical purposes, such as detection of pathologies based on the auditory properties of the cry signal (e.g., [37,38]), or assessment of the neurological state of infants based on differences in the crying between full-term and preterm babies [39].…”

Section: Approaches In Audio Event Detectionmentioning

confidence: 99%

Development and Analysis of Deep Learning Architectures

2020

Studies in Computational Intelligence

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In this chapter, we compare deep learning and classical approaches for detection of baby cry sounds in various domestic environments under challenging signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network (CNN) architectures for baby cry detection, and compare their performance to that of classical machine-learning approaches, such as logistic regression and support vector machines. In addition to feedforward CNNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized nonsymmetric kernels, better results are obtained compared to common CNN architectures.

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“…Radhika estimated the fundamental and formant frequencies of infants cries with congenital heart disorder using frequency domain (Cepstrum) and linear prediction code (LPC) methods [11]. Cry is a common response that being investigated related to pain [12][13][14], hungry [13][14][15], no-pain, discomfort [1], apnea [2], asphyxia [16][17][18][19][20], hypothyroidism [19,21], Hyperbilirubinemia [22], cleft palate [13,23], Ankyloglossia [24], respiratory distress syndrome [13], hearing disorder [25], brain damage [3], hydrocephalus [26] and sudden infant death syndrome (SIDS) [27].…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition System of Infant Cry based on F-Transform

Al-Azzawi¹

2014

IJCA

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A new approach has been proposed to design of an automatic infant cry recognition system based on the fuzzy transform (F-transform) that classifies two different kinds of cries, which come from physiological status and medical disease. Feature extraction and development of classification algorithms play an important role in the area of automatic analysis of infant cry signals. F-transform is the powerful tool for the approximation of continuous functions on a finite domain and has been used as feature extraction of infant cry signals. Neural networks is developed to classify the infant cry signals into physiological status and medical disease and trained with smoothing parameter to obtain better classification accuracy. Neural networks (recognition system) are trained from the random selection of crying babies. The experimental results show that the proposed give very promising classification accuracy of 96%. The proposed method can be used to assist medical investigation of the infant from the cry signals.

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Classification of Infant Cries with Asphyxia Using Multilayer Perceptron Neural Network

Cited by 29 publications

References 8 publications

A review of infant cry analysis and classification

A review of infant cry analysis and classification

Development and Analysis of Deep Learning Architectures

Automatic Recognition System of Infant Cry based on F-Transform

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