Design a Filter Using MATLAB

Procedure 1:
FS = 22050; % voice signal sampling frequency: 22050
X1 = wavread ('windows critical stop.wav '); % reads audio signal data and assigns it to the variable X1
Sound (x1, 22050); % play voice signal
Y1 = FFT (x1, 1024); % perform 1024-point FFT transformation on the signal
F = FS * (0: 511)/1024;
Figure (1)
Plot (X1) % to make the time domain image of the original voice signal
Title ('original speech sign ');
Xlabel ('time n ');
Ylabel ('fuzhi n ');

Figure (2)
Freqz (X1) % plot the frequency response diagram of the original voice signal
Title ('frequency response fig ')

Figure (3)
Subplot (2, 1, 1 );
Plot (ABS (Y1 () % to do the FFT Spectrum of the original voice signal
Title ('fft spectrum of original speech signal ')
Subplot (2, 1, 2 );
Plot (F, ABS (Y1 (1:512 )));
Title ('original Speech Signal Spectrum ')
Xlabel ('hz ');
Ylabel ('fuzhi ');

Procedure 2:
FS = 22050; % voice signal sampling frequency: 22050
X1 = wavread ('windows critical stop.wav '); % reads audio signal data and assigns it to the variable X1
T =/22050 :( size (X1)-1)/22050;
Y1 = FFT (x1, 1024); % perform 1024-point FFT transformation on the signal
F = FS * (0: 511)/1024;
X2 = randn (1, length (X1); % generates a random signal with the same length as X
Sound (X2, 22050 );
Figure (1)
Plot (X2) % to make the time domain image of the original voice signal
Title ('gaussian random Noise ');
Xlabel ('time n ');
Ylabel ('fuzhi n ');

Randn ('state', 0 );
M = randn (SIZE (X1 ));
X2 = 0.1 * m + x1;

Sound (X2, 22050); % voice signal after noise is played
Y2 = FFT (X2, 1024 );
Figure (2)
Plot (T, X2)
Title ('noisy speech sign ');
Xlabel ('time n ');
Ylabel ('fuzhi n ');
Figure (3)
Subplot (2, 1, 1 );
Plot (F, ABS (Y2 (1:512 )));
Title ('original Speech Signal Spectrum ');
Xlabel ('hz ');
Ylabel ('fuzhi ');
Subplot (2, 1, 2 );
Plot (F, ABS (Y2 (1:512 )));
Title ('noisy Speech Signal Spectrum ');
Xlabel ('hz ');
Ylabel ('fuzhi ');

Based on the above code, you can modify the following error code
Procedure 3: design a Butterworth Filter Using Bilinear Transformation

FS = 22050;
X1 = wavread ('H: \ Course Design 2 \ shuzi.wav ');
T =/22050 :( size (X1)-1)/22050;
Au = 0.03;
D = [Au * Cos (2 * pI * 5000 * t)] ';
X2 = X1 + D;
WP = 0.25 * PI;
Ws = 0.3 * PI;
Rp = 1;
Rs = 15;
FS = 22050;
TS = 1/Fs;
WP1 = 2/TS * Tan (WP/2); % converts a simulated indicator to a digital indicator
WS1 = 2/TS * Tan (WS/2 );
[N, Wn] = buttord (WP1, WS1, RP, RS, 's'); % select the minimum order of the filter
[Z, P, K] = buttap (n); % create a Butterworth analog filter
[BAP, AAP] = zp2tf (z, P, K );
[B, A] = lp2lp (BAP, AAP, wn );
[BZ, AZ] = bilinear (B, A, FS); % use bilinear transform to convert a simulated filter to a digital filter
[H, w] = freqz (BZ, AZ); % plot the frequency response curve
Figure (1)
Plot (w * fs/(2 * PI), ABS (h ))
Grid
Xlabel ('frequency/Hz ')
Ylabel ('frequency response amplitude ')
Title ('butterws ')
F1 = filter (BZ, AZ, X2 );
Figure (2)
Subplot (2, 1, 1)
Plot (T, X2) % to plot the time domain diagram before Filtering
Title ('time Domain Waveform before filtering ');
Subplot (2, 1, 2)
Plot (T, F1); % plot the filtered Time Domain Diagram
Title ('time Domain Waveform after filter ');
Sound (F1, 22050); % the filtered signal
F0 = FFT (F1, 1024 );
F = FS * (0: 511)/1024;
Figure (3)
Y2 = FFT (X2, 1024 );
Subplot (2, 1, 1 );
Plot (F, ABS (Y2 (); % plot the spectrum before Filtering
Title ('spectrum before filter ')
Xlabel ('hz ');
Ylabel ('fuzhi ');
Subplot (2, 1, 2)
F1 = plot (F, ABS (f0 (); % plot the filtered Spectrum
Title ('spectrum after filter ')
Xlabel ('hz ');
Ylabel ('fuzhi ');

Procedure 4: design the filter using the Window Function Method:

FS = 22050;
X1 = wavread ('H: \ Course Design 2 \ shuzi.wav ');
T =/22050 :( size (X1)-1)/22050;
Au = 0.03;
D = [Au * Cos (2 * pI * 5000 * t)] ';
X2 = X1 + D;
WP = 0.25 * PI;
Ws = 0.3 * PI;
Wdelta = WS-WP;
N = Ceil (6.6 * PI/wdelta); % integer
Wn = (0.2 + 0.3) * PI/2;
B = FIR1 (n, Wn/PI, Hamming (n + 1); % select the window function and normalize the cutoff frequency
Figure (1)
Freqz (B, 1,512)
F2 = filter (BZ, AZ, X2)
Figure (2)
Subplot (2, 1, 1)
Plot (T, X2)
Title ('time Domain Waveform before filtering ');
Subplot (2, 1, 2)
Plot (T, F2 );
Title ('time Domain Waveform after filter ');
Sound (F2, 22050); % audio signal after filtering
F0 = FFT (F2, 1024 );
F = FS * (0: 511)/1024;
Figure (3)
Y2 = FFT (X2, 1024 );
Subplot (2, 1, 1 );
Plot (F, ABS (Y2 (1:512 )));
Title ('spectrum before filter ')
Xlabel ('hz ');
Ylabel ('fuzhi ');
Subplot (2, 1, 2)
F2 = plot (F, ABS (f0 (1:512 )));
Title ('spectrum after filter ')
Xlabel ('hz ');
Ylabel ('fuzhi ');