Speech Processing, Proj02

//Purdue Cal - ECE 595C.
//Spring 2010.
//Project 02.
//Copyright @ 2010 antonio081014  antonio081014 ;
//All codes are in Matlab.


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Task 1: Obtain estimated F0  by using the log harmonic product spectrum with K = 4 and K=10.

PS: The log product of the harmonic spectrum is the summation of the log harmonic spectrum.\

1. Down sampling with K=4 and K=10 to get the spectrum for each case.

2. Sum them up.

3. Find the highest peak in the final spectrum. That is the estimated F0.

Code:

fdate = fft(dat(:,15).*hamming(320), 1024);  % The 15th frame in the data.

tmp = log(abs(fdat3(1:512)));

figure;

subplot(4,1,1);

plot(tmp);

title(['The frequency plot of the frame #:', num2str(23)]);

xlabel('Freq index');

ylabel('Log-Magnitude');

hold on;

tmp1 = downsample(tmp, 4);

z = zeros(512 - length(tmp1),1);

tmp1 = [tmp1; z];

tmp2 = downsample(tmp, 10);

z = zeros(512 - length(tmp2),1);

tmp2 = [tmp2; z];

subplot(4,1,2);

plot(tmp1, 'r');

xlabel('Freq index, K=4');

ylabel('Log-Magnitude');

subplot(4,1,3);

plot(tmp2, 'g');

xlabel('Freq index, K=10');

ylabel('Log-Magnitude');

product = tmp + tmp1 + tmp2;

subplot(4,1,4);

plot(product, 'r');

xlabel('Freq index');

ylabel('Log-Magnitude');

[x, y] = ginput(3);

% x = [11.79; 57.60; 93.18;]; These are the index in frequency domain.

Task 2: Using overlap-add method, reconstruct the given utterance after filtering each frame using a bandpass filter from 50Hz-2000Hz.

1. Filter each frame.

2. Reconstruct the utterance by using overlap-add method.

Code:

% Filter:

recdat = zeros(320, 100);

for i=1:100                                    % There is 100 frames here.

    fmdate = dat(:,i);

    ffmdat = fft(fmdate.*hamming(320),1024); % Take Fourier Transform using 1024 points.

    LF = ceil(1024 / fs * 50);

    HF = floor(1024 / fs * 2000);

    ffmdat(1:LF,1) = 0;

    ffmdat(HF:512,1) = 0;

    ffmdat(512:end-HF,1) = 0;

    ffmdat(end-LF:end,1) = 0;

    recdat(:,i) = real(ifft(ffmdat,320));

    clear ffmdat;

end

% Reconstruct

update_sp = zeros(1,16000);

start = 1;

stop = start + 320 - 1;

for i=1:100

    update_sp(start:stop) = update_sp(start:stop) + recdat(:,i)';    % overlap-add;

    start = start + 160;

    stop = start + 320 - 1;

    if stop > 16000

        break

    end

end

figure;

plot(update_sp);

title('The filterd speech.');

% soundsc(update_sp);

fm = recdat(:,23);

ffm = fft(fm.*hamming(320), 1024);

figure;

plot(abs(ffm(1:512)));

title(['The filterd frame #:', num2str(23)]);

[xx, yy] = ginput(3);

% xx = 35.2534562211982;72.5806451612903;107.142857142857;];

%%

figure;

x = nn_spch;

window = 128;

noverlap = 64;

nfft = 128;

fs = 16000;

spectrogram(x,window,noverlap,nfft,fs, 'yaxis');

colormap(gray);

title('Power Spectral Density, Original');

xlabel('In Time Domain.');

ylabel('In Freq Domain.');

figure;

x = update_sp;

window = 128;

noverlap = 64;

nfft = 128;

fs = 16000;

spectrogram(x,window,noverlap,nfft,fs, 'yaxis');

colormap(gray);

title('Power Spectral Density, Filterd');

xlabel('In Time Domain.');
ylabel('In Freq Domain.');