Selection of Optimum Mother Wavelet Function for Turkish Phonemes

Engin, Erkan Zeki; Arslan, Özkan

doi:10.18100/ijamec.556850

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Meanwhile, since various scaling factors and features of the wavelet functions are available, the optimal selection procedure of the mother wavelet function is a significant step to the wavelet denoising method [ 26 , 27 ]. Traditionally, the mother wavelet function is selected to represent the characteristics of the signal by empirical research [ 28 , 29 ]. Since this method does not provide the actual optimal selection of the mother wavelet [ 30 ], studies of the mother wavelet selection for various signals such as the electroencephalography (EEG) signal, vibration signal of turbine, human voice signal, and many kinds of radar signal have been occurring [ 26 , 28 , 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

“…Traditionally, the mother wavelet function is selected to represent the characteristics of the signal by empirical research [ 28 , 29 ]. Since this method does not provide the actual optimal selection of the mother wavelet [ 30 ], studies of the mother wavelet selection for various signals such as the electroencephalography (EEG) signal, vibration signal of turbine, human voice signal, and many kinds of radar signal have been occurring [ 26 , 28 , 31 , 32 , 33 , 34 , 35 , 36 ]. However, to the best of the author’s knowledge, studies on the optimal selection of the mother wavelet function and the decomposition level for the denoising of the DCG signal are rarely reported.…”

Section: Introductionmentioning

confidence: 99%

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The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

Jang

Sim

Yang

et al. 2021

Sensors

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The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system. To select the best suited mother wavelet function and wavelet decomposition level, this paper presents the quantitative analysis results. Both the optimal mother wavelet and decomposition level are selected by evaluating signal-to-noise-ratio (SNR) efficiency of the denoised signals obtained by using the wavelet thresholding method. A total of 115 potential functions from six wavelet families were examined for the selection of the optimal mother wavelet function and 10 levels (1 to 10) were evaluated for the choice of the best decomposition level. According to the experimental results, the most efficient selections of the mother wavelet function are “db9” and “sym9” from Daubechies and Symlets families, and the most suitable decomposition level for the used signal is seven. As the evaluation criterion in this study rates the efficiency of the denoising process, it was found that a mother wavelet function longer than 22 is excessive. The experiment also revealed that the decomposition level can be predictable based on the frequency features of the DCG signal. The proposed selection of the mother wavelet function and the decomposition level could reduce noise effectively so as to improve the quality of the DCG signal in information field.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

Jang

Sim

Yang

et al. 2021

Sensors

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Features Extraction on Cleft Lip Speech Signal using Discrete Wavelet Transformation

Yusuf,

Dinata

2024

jtim

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Cleft is one of the most common birth defects worldwide, including in Indonesia. In Indonesia, there are 1,596 cleft patients, with 50.53% having a cleft lip and palate (CL/P), 24.42% having a cleft lip (CL), and 25.05% having a cleft palate (CP). Individuals with clefts encounter difficulties with resonance and articulation during communication due to dysfunctions in the oral and nasal cavi-ties. This study investigates various types of mother wavelets as feature extractors for cleft speech signals. Five different mother wavelets, namely Symlet order 2, Reverse Biorthogonal order 1.1, Discrete Meyer, Coiflet order 1, and Biorthogonal order 1.1 are analyzed. This work aims to find the best type of mother wavelet. The extracted features are statistical features, such as mean, me-dian, standard deviation, kurtosis, and skewness. The dataset used in this study consists of 200 sound signals from 10 individuals with cleft conditions and 10 normal volunteers. To assess the performance of the extractor, classification is performed using K-Nearest Neighbor (KNN) and K-Fold cross-validation. The experimental results indicate that the Reverse Biorthogonal order 1.1 mother wavelet achieves the highest accuracy compared to other types of mother wavelet, where the accuracy is 93%, with sensitivity and specificity of 94% and 92%, respectively.

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Selection of Optimum Mother Wavelet Function for Turkish Phonemes

Cited by 2 publications

References 30 publications

The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

Features Extraction on Cleft Lip Speech Signal using Discrete Wavelet Transformation

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