A new algorithm is proposed to compress speech signals using wavelet transform and linear predictive coding. Signal compression based on the concept of selecting a small number of approximation coefficients after they are compressed by the wavelet decomposition (Haar and db4) at a suitable chosen level and ignored details coefficients, and then approximation coefficients are windowed by a rectangular window and fed to the linear predictor. Levinson Durbin algorithm is used to compute LP coefficients, reflection coefficients and predictor error. The compress files contain LP coefficients and previous sample. These files are very small in size compared to the size of the original signals. Compression ratio is calculated from the size of the compressed signal relative to the size of the uncompressed signal. The proposed algorithms where fulfilled with the use of Matlab package
Speech Signal Compression Using Wavelet And Linear Predictive Coding
Keyword: Wavelet
Speech Coding
Linear Predictive Coding
Levinson Durbin.
Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Economics And Administrative Sciences
Fuzzy Bridge Regression Model Estimating via Simulation
Fuzzy linear regression
Fuzzy least squares method
Fuzzy Bridge regression
multicollinearity
triangular fuzzy numbers
Variance Inflation Factor.
الانحدار الخطي الضبابي
طريقة المربعات الصغرى الضبابية
انحدار الجسر الضبابي
التعدد الخطي
الأرقام الضبابية المثلثية
عامل تضخم التباين.
The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin
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