A comparison of neural network architectures for the classification of three types of infant cry vocalizations

Petroni, M.; Malowany, A.S.; Johnston, Céleste; Stevens, Bonnie

doi:10.1109/iembs.1995.575380

Cited by 33 publications

(7 citation statements)

References 5 publications

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“…This range was chosen based on commonly used values in literature [1,[3][4][5]. Therefore, the values of ‫݊݅ܯݕ‬ and ‫ݔܽܯݕ‬ for ‫‬ was 20 and 40, respectively and the values of ‫݊݅ܯݕ‬ and ‫ݔܽܯݕ‬ for ݊ was 10 and ‫,‬ respectively.…”

Section: Optimization Of Mfcc Computation Using Dmpsomentioning

confidence: 99%

“…Numerous investigations have proven that different types of cry signals can be discriminated using Mel Frequency Cepstral Coefficient (MFCC) and Multi-Layer Perceptron (MLP) classifiers [3]. MFCC is a feature extraction method that produces coefficients, which constitute a good representation of dominant features in the signal using a selected time window.…”

Section: Introductionmentioning

confidence: 99%

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Discrete Mutative Particle Swarm Optimisation of MFCC computation for classifying hypothyroidal infant cry

Zabidi

Mansor

Khuan

et al. 2010

2010 International Conference on Computer Applications and Industrial Electronics

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This paper describes the optimization of Mel Frequency Cepstral Coefficients (MFCC) parameters using Discrete Mutative Particle Swarm Optimization (DMPSO) for diagnosis of hypothyroidism in infants. The MFCC was used to extract the feature set from infant cry signals. The features were then classified using Multi-Layer Perceptron (MLP). The DMPSO variants optimize the number of filter banks and number of cepstral coefficients in MFCC. Based on the values chosen by DMPSO, the extracted features were then fed to 50 MLP classifiers (with different initial weight initialization values), which were trained to discriminate between healthy and hypothyroid infants. The results showed that DMPSO managed to produce classification accuracy of 88.7% with percentage convergence of 66.7% in detecting hypothyroidism from infant cry signals. The optimal number of filter bank and MFC coefficients were found to be 36 and 19 respectively.

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Section: Optimization Of Mfcc Computation Using Dmpsomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discrete Mutative Particle Swarm Optimisation of MFCC computation for classifying hypothyroidal infant cry

Zabidi

Mansor

Khuan

et al. 2010

2010 International Conference on Computer Applications and Industrial Electronics

View full text Add to dashboard Cite

show abstract

“…The performance of Feed Forward Neural Network (FFNN), Recurrent Neural Network (RNN) and TDNN in recognizing pain, fear and hunger from infant cries has also been investigated. Among the three techniques, FFNN was the most accurate, however it could only provide the highest classification accuracy of 69.23% [2].…”

Section: Introductionmentioning

confidence: 96%

“…One of the classification techniques namely Artificial Neural Network (ANN) is able to classify and group various types of cry [1][2][3]. Time Delay Neural Network (TDNN) has been used to differentiate cries between normal, deaf and asphyxiated infants.…”

Section: Introductionmentioning

confidence: 99%

The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron Neural Network

Zabidi

Mansor

Khuan

et al. 2010

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

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Artificial Neural Network has been widely applied for solving pattern recognition problems including infant cry classification for detecting infant health and physical status. Feature extraction is usually performed using Mel Frequency Cepstrum Coefficient (MFCC) analysis. If irrelevant features in the MFCC are not removed, the performance of the MLP will be degraded. The use of F-ratio is essential to select the significant features. This paper examines the effect of selecting features using F-ratio on the classification accuracy of the MLP. Results obtained from direct selection of coefficients and selection of coefficients via F-ratio, were compared. It is found that the contribution of F-ratio in the selection of input for the MLP has managed to produce high classification accuracy.

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“…All researchers who are working in this field have their own database. In [2,3], researchers have collected data from 16 healthy infants for cry classification. In [4] , cry is analyzed for hearing disorder detection with 37 infants out of which 14 had severe hearing loss and 23 had normal hearing.…”

Section: Introductionmentioning

confidence: 99%

Data collection and corpus design for analysis of nonnal and pathological infant cry

Chittora

Patil

2013

2013 International Conference Oriental COCOSDA Held Jointly With 2013 Conference on Asian Spoken Language Research and Evaluati

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Cry is the first response of an infant as a result of interaction with external world. Till an infant learns to speak, it is the only communicating tool with which he/she can express his/her needs and ask for attention. Infant cry is an important tool for analysis of an infants' health condition. To understand the requirements of an infant (such as feeding, boredom, and health-related issues) acoustic analysis of an infant cry is essential. In this paper, the development of infant cry corpus is presented. Infant cry corpus is designed for the analysis of different types of cry modes, cry melodies, and assessment of cry from clinical diagnosis perspective. In this paper, along with database description spectrographic analysis for results is also reported on the designed corpus to signify the importance of cry as a tool for clinical analysis of infant health.

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A comparison of neural network architectures for the classification of three types of infant cry vocalizations

Cited by 33 publications

References 5 publications

Discrete Mutative Particle Swarm Optimisation of MFCC computation for classifying hypothyroidal infant cry

Discrete Mutative Particle Swarm Optimisation of MFCC computation for classifying hypothyroidal infant cry

The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron Neural Network

Data collection and corpus design for analysis of nonnal and pathological infant cry

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