Detection of infant hypothyroidism with mel frequency cepstrum analysis and multi-layer perceptron classification

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

doi:10.1109/cspa.2010.5545331

Cited by 10 publications

(11 citation statements)

References 5 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%

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%

A review of infant cry analysis and classification

Mudiyanselage

Gao

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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“…The factor of choosing one hidden neuron as the best neuron was the accuracy value achieved accurately more than 80% with minimum MSE value at ISSN: 2088-8708  Modeling of agarwood oil compounds based on linear regression and ANN … (Noratikah Zawani Mahabob) 5511 the early stage of training. Also, one hidden neuron is sufficient to avoid long computational time and overfitting problems [17], [18]. The results of MSE for each hidden neuron for all training algorithms shared the same average value.…”

Section: Mlp Networkmentioning

confidence: 99%

“…The only similarities in both research studies are the results obtained showed that LM algorithm performed good accuracy with minimum error. Researcher [18] successfully implemented MLP to detect infant hypothyroidism using cry signal that extracts using MFCC analysis. Different number of hidden neurons and the number of coefficients are varied in this experiment as well as a scaled conjugate gradient (SCG) as training algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Different number of hidden neurons and the number of coefficients are varied in this experiment as well as a scaled conjugate gradient (SCG) as training algorithm. The output obtained concluded that the lowest MSE and highest accuracy performed at hidden neurons 15 with the 20 feature coefficients [18]. Based on existing work in [19] and [20], the MLP network is used in pattern recognition in which recognize 18 leaves images by classifying the defected and un-defected leaves and classifying the resume according to the job specification by word recognition.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification

Mahabob

Yusoff

Amidon

et al. 2021

IJECE

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<span>Agarwood oil is in increasing demand in Malaysia throughout the world for use in incense, traditional medicine, and perfumes. However, there is still no standardized grading method for agarwood oil. It is vital to grade agarwood oil into high and low quality so that both qualities can be properly differentiated. In the present study, data were obtained from the Forest Research Institute Malaysia (FRIM), Selangor Malaysia and Bioaromatic Research Centre of Excellence (BARCE), Universiti Malaysia Pahang (UMP). The work involves the data from a previous researcher. As a part of on-going research, the stepwise linear regression and multilayer perceptron have been proposed for grading agarwood oil. The output features of the stepwise regression were the input features for modeling agarwood oil in a multilayer perceptron (MLP) network. A three layer MLP with 10 hidden neurons was used with three different training algorithms, namely resilient backpropagation (RBP), levenberg marquardt (LM) and scaled-conjugate gradient (SCG). All analytical work was performed using MATLAB software version R2017a. It was found that one hidden neuron in LM algorithm performed the most <span>accurate result in the classification of agarwood oil with the lowest mean squared error (MSE) as compared to SCG and RBP algorithms. The findings in this research will be a</span> benefit for future works of agarwood oil research areas, especially in terms of oil quality classification.</span>

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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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Detection of infant hypothyroidism with mel frequency cepstrum analysis and multi-layer perceptron classification

Cited by 10 publications

References 5 publications

A review of infant cry analysis and classification

A review of infant cry analysis and classification

Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification

Automatic Recognition System of Infant Cry based on F-Transform

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