Binary Particle Swarm Optimization for selection of features in the recognition of infants cries with asphyxia

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

doi:10.1109/cspa.2011.5759886

Cited by 15 publications

(5 citation statements)

References 6 publications

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“…The MLP classifier has a similar performance to the SVM, the samples are classified by constructing a complex decision boundary. MLP was successfully applied to several studies regarding asphyxia, which also involves the respiratory system [ 58 , 59 , 60 ]. Hence, it would be beneficial to investigate MLP in the diagnosis of RDS as well.…”

Section: Methodsmentioning

confidence: 99%

Newborn Cry-Based Diagnostic System to Distinguish between Sepsis and Respiratory Distress Syndrome Using Combined Acoustic Features

2022

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Crying is the only means of communication for a newborn baby with its surrounding environment, but it also provides significant information about the newborn’s health, emotions, and needs. The cries of newborn babies have long been known as a biomarker for the diagnosis of pathologies. However, to the best of our knowledge, exploring the discrimination of two pathology groups by means of cry signals is unprecedented. Therefore, this study aimed to identify septic newborns with Neonatal Respiratory Distress Syndrome (RDS) by employing the Machine Learning (ML) methods of Multilayer Perceptron (MLP) and Support Vector Machine (SVM). Furthermore, the cry signal was analyzed from the following two different perspectives: 1) the musical perspective by studying the spectral feature set of Harmonic Ratio (HR), and 2) the speech processing perspective using the short-term feature set of Gammatone Frequency Cepstral Coefficients (GFCCs). In order to assess the role of employing features from both short-term and spectral modalities in distinguishing the two pathology groups, they were fused in one feature set named the combined features. The hyperparameters (HPs) of the implemented ML approaches were fine-tuned to fit each experiment. Finally, by normalizing and fusing the features originating from the two modalities, the overall performance of the proposed design was improved across all evaluation measures, achieving accuracies of 92.49% and 95.3% by the MLP and SVM classifiers, respectively. The MLP classifier was outperformed in terms of all evaluation measures presented in this study, except for the Area Under Curve of Receiver Operator Characteristics (AUC-ROC), which signifies the ability of the proposed design in class separation. The achieved results highlighted the role of combining features from different levels and modalities for a more powerful analysis of the cry signals, as well as including a neural network (NN)-based classifier. Consequently, attaining a 95.3% accuracy for the separation of two entangled pathology groups of RDS and sepsis elucidated the promising potential for further studies with larger datasets and more pathology groups.

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Section: Methodsmentioning

confidence: 99%

Newborn Cry-Based Diagnostic System to Distinguish between Sepsis and Respiratory Distress Syndrome Using Combined Acoustic Features

2022

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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%

“…Forward variable Selection Method (FSM) was applied to infant cry classification by Wang in 2010 [55] and Okada et al proposed Iterative FSM (IFSM) based on cross-validation concept in 2011 [54]. Later, Binary Particle Swarm Optimization (BPSO) was used to remove the redundant features and keep the significant features from MFCC coefficients in [61,62]. Orlandi et al used a software called Biovoice to extract 22 features from the cry signal and then used a genetic algorithm-based search method to select the best features to feed to the classifiers [21].…”

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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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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“…They mimic biological evolution, in that they optimise a set of candidate feature vectors over several iterations, or generations, taking into account the quality of past candidates. Evolutionary feature selection has been used successfully in the audiovisual domain, for instance for instrument recognition [16], voice command recognition [17], classification of infant cries [18], speaker identification [19], and emotion prediction [20,21].…”

Section: Introductionmentioning

confidence: 99%

An ‘End-to-Evolution’ Hybrid Approach for Snore Sound Classification

Freitag¹,

Amiriparian²,

Cummins³

et al. 2017

Interspeech 2017

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Whilst snoring itself is usually not harmful to a person's health, it can be an indication of Obstructive Sleep Apnoea (OSA), a serious sleep-related disorder. As a result, studies into using snoring as acoustic based marker of OSA are gaining in popularity. Motivated by this, the INTERSPEECH 2017 ComParE Snoring sub-challenge requires classification from which areas in the upper airways different snoring sounds originate. This paper explores a hybrid approach combining evolutionary feature selection based on competitive swarm optimisation and deep convolutional neural networks (CNN). Feature selection is applied to novel deep spectrum features extracted directly from spectrograms using pre-trained image classification CNN. Key results presented demonstrate that our hybrid approach can substantially increase the performance of a linear support vector machine on a set of low-level features extracted from the Snoring sub-challenge data. Even without subset selection, the deep spectrum features are sufficient to outperform the challenge baseline, and competitive swarm optimisation further improves system performance. In comparison to the challenge baseline, unweighted average recall is increased from 40.6 % to 57.6 % on the development partition, and from 58.5 % to 66.5 % on the test partition, using 2 246 of the 4 096 deep spectrum features.

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Binary Particle Swarm Optimization for selection of features in the recognition of infants cries with asphyxia

Cited by 15 publications

References 6 publications

Newborn Cry-Based Diagnostic System to Distinguish between Sepsis and Respiratory Distress Syndrome Using Combined Acoustic Features

Newborn Cry-Based Diagnostic System to Distinguish between Sepsis and Respiratory Distress Syndrome Using Combined Acoustic Features

A review of infant cry analysis and classification

An ‘End-to-Evolution’ Hybrid Approach for Snore Sound Classification

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