On Analysis of Suitable Wavelet Family for Processing of Cough Signals

2019 ◽  
pp. 194-200
Author(s):  
Agam Srivastava ◽  
Vikrant Bhateja ◽  
Adya Shankar ◽  
Ahmad Taquee
Keyword(s):  
Wavelet Family

Compression and denoising of speech transmission using Daubechies wavelet family

2017 ◽  
Vol 12 (4) ◽  
pp. 313
Author(s):  
G. Mohiuddin Bhat ◽  
Shabir A. Parah ◽  
Sakeena Akhtar ◽  
Javaid A. Sheikh
Keyword(s):  
Daubechies Wavelet ◽  
Speech Transmission ◽  
Wavelet Family

scholarly journals Determination of the Optimum Wavelet Basis Function for Indonesian Vowel Voice Recognition

2017 ◽  
Vol 17 (2) ◽  
pp. 42 ◽  
Author(s):  
Syahroni Hidayat ◽  
Habib Ratu P. N. ◽  
Danang Tejo Kumoro
Keyword(s):  
Cross Correlation ◽  
Recognition System ◽  
Basis Functions ◽  
Wavelet Basis ◽  
Trial And Error ◽  
Wavelet Base ◽  
Automatic Speech Recognition System ◽  
Base Function ◽  
Wavelet Family

Nowadays, wavelet has been widely applied in extracting features of the signal for automatic speech recognition system. Wavelets have many families that are determined by their mother function and order. The use of different wavelets to analyze the same signal would bring different results. In many cases, a trial and error procedure is used to select the optimal wavelet family. That is because there are no particular wavelet basis functions that can be applied to the entire speech signals. Therefore, it is necessary to analyze the similarity between the speech signal and the wavelet base function. One of the methods that can be used is cross-correlation. In this study, the degree of correlation is determined between wavelet base function and Indonesian vowels. The influence of gender and consistencies of the results are also used in the analysis. The results show that db45 and db44 are most similar to male and female vowels utterance, respectively. For consistencies, only vowel e gives a consistent result. Overall, db44 is most similar to all Indonesian vowels utterance.

scholarly journals General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091100 ◽  
Author(s):  
Ahmad al-Qerem ◽  
Faten Kharbat ◽  
Shadi Nashwan ◽  
Staish Ashraf ◽  
khairi blaou
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Differential Evolution ◽  
Discrete Wavelet Transform ◽  
Research Work ◽  
Discrete Wavelet ◽  
Proposed Model ◽  
Feature Extraction And Selection ◽  
Selection Step ◽  
Wavelet Family

Wavelet family and differential evolution are proposed for categorization of epilepsy cases based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in feature extraction step because it efficiently works in this field, as confirmed by the results of previous studies. The feature selection step is used to minimize dimensionality by excluding irrelevant features. This step is conducted using differential evolution. This article presents an efficient model for EEG classification by considering feature extraction and selection. Seven different types of common wavelets were tested in our research work. These are Discrete Meyer (dmey), Reverse biorthogonal (rbio), Biorthogonal (bior), Daubechies (db), Symlets (sym), Coiflets (coif), and Haar (Haar). Several kinds of discrete wavelet transform are used to produce a wide variety of features. Afterwards, we use differential evolution to choose appropriate features that will achieve the best performance of signal classification. For classification step, we have used Bonn databases to build the classifiers and test their performance. The results prove the effectiveness of the proposed model.

scholarly journals A new wavelet family based on second-order LTI-systems

2016 ◽  
Vol 24 (10) ◽  
pp. 1967-1986 ◽  
Author(s):  
Tariq Abuhamdia ◽  
Saied Taheri ◽  
John Burns
Keyword(s):  
Second Order ◽  
Family Based ◽  
Wavelet Family

A new wavelet family for speckle noise reduction in medical ultrasound images

Measurement ◽  
2019 ◽  
Vol 140 ◽  
pp. 572-581 ◽  
Author(s):  
Andréia Seixas Leal ◽  
Henrique Mohallem Paiva
Keyword(s):  
Noise Reduction ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Speckle Noise Reduction ◽  
Wavelet Family

The Morlet and Daubechies Wavelet Transforms for Fatigue Strain Signal Analysis

2013 ◽  
Vol 471 ◽  
pp. 197-202 ◽  
Author(s):  
T.E. Putra ◽  
S. Abdullah ◽  
Mohd Zaki Nuawi ◽  
Mohd Faridz Mod Yunoh
Keyword(s):  
Wavelet Transforms ◽  
Signal Analysis ◽  
Wavelet Coefficient ◽  
Discrete Wavelet ◽  
Daubechies Wavelet ◽  
Wavelet Coefficients ◽  
Public Road ◽  
Road Surfaces ◽  
Strain Signal ◽  
Wavelet Family

This paper presents the convenient wavelet family for the fatigue strain signal analysis based on the wavelet coefficients. This study involves the Morlet and Daubechies wavelet coefficients using both the Continuous and Discrete Wavelet Transforms, respectively. The signals were collected from a front lower suspension arm of a passenger car by placing strain gauges at the highest stress locations. The car was driven over public road surfaces, i. e. pavé, highway and UKM roads. In conclusion, the Daubechies wavelet was the convenient wavelet family for the analysis. It was because the wavelet gave the higher wavelet coefficient values indicating that the resemblance between the wavelet and the signals was stronger, closer and more similar.

Neville–Lagrange wavelet family for lossless image compression

2008 ◽  
Vol 88 (11) ◽  
pp. 2833-2842 ◽  
Author(s):  
Dong Chen ◽  
Tianwen Zhang
Keyword(s):  
Image Compression ◽  
Lossless Image Compression ◽  
Wavelet Family
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