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Unformatted text preview: Linear Predictive Coding Methods Speech Processing ECE 5525 Final Project Report By: Mohamed M. Eljhani Fall 2010 Problem Description The main purpose of this project is to show the different between three linear predictive methods by implementing a Matlab program that convert from a frame of speech to a set of linear Prediction coefficients, using three basic methods, i.e. The Autocorrelation Method. The Covariance Method. The Lattice Filter Method. Choose a section of a steady state vowel, and a section of unvoiced speech, to plot LPC spectra from the three methods along with the normal spectrum from the Hamming window weighted frame. Write a MATLAB program to convert from a frame of speech to a set of linear prediction coefficients, using 3 methods, i.e., the Autocorrelation Method, the Covariance Method, and the Lattice Filter Method. Choose a section of a steady state vowel, and a section of unvoiced speech, and plot LPC spectra from the 3 methods along with the normal spectrum from the Hamming window weighted frame. Use N = 300, p = 12, with Hamming Window weighting for the autocorrelation method. Use the same parameters for the Covariance and Lattice Methods. Use the files ah.wav to get a vowel steady state sound beginning at sample 3000, and the file test 16k.wav to get a fricative beginning at sample 3000. (Note that for the covariance and lattice methods, you also need to preserve p samples before the starting sample at n = 3000 for computing correlations, and error signals). Problem Solution The following MATLAB program reads in a file of speech and computes the original spectrum (of the signal weighted by a Hamming window), and plots on top of this the LPC spectrum from the autocorrelation, covariance, and lattice methods. There is a main program and three functions (durbin for the auto correlation method, cholesky for the covariance method although simple matrix inversion was used, rather than the Cholesky decomposition, and lattice for the traditional lattice method). % % read in speech file, chooses section of speech and solve for set of % lpc coefficients using the autocorrelation method, the covariance % method and the lattice method % % plot the resulting spectra from all three methods % % read in waveform for a speech file % % [xin,fs,mode,format]=loadwav('ah_truncated.wav');xin) filename=input ('enter speech filename:','s'); [xin,fs,mode,format]=loadwav(filename); % normalize input to [1,1] range and play out sound file...
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 Fall '10
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