50 lines
1.4 KiB
Matlab
50 lines
1.4 KiB
Matlab
%% Inicialização do ambiente
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clear; close all; clc
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%% Exercício 8
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% Input
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[y, Fs] = audioread("Canto1.mp3"); % Signal and Sampling Frequency
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signal = y(:,1);
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Fn = Fs/2; % Nyquist Frequency (Hz)
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%Ts = 1/Fs;
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L = length(signal); % Signal Length
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% Find the noise
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FTsignal = fft(signal)/L; % Fourier Transform
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Fv = linspace(0, 1, fix(L/2)+1)*Fn; % Frequency Vector
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Iv = 1:numel(Fv); % Index Vector
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figure(1)
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% x is Hz, y is amplitude
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plot(Fv, abs(FTsignal(Iv))*2)
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grid
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% Butterworth Low-Pass Order 7
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%[num, den] = butter(7, fc, 'low'); %order, cutoff frequency, type
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%[SOS,G] = tf2sos(num, den); % Convert To Second-Order-Section For Stability
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%figure(1)
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%freqz(SOS, 4096, Fs) % Check Filter Performance
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%s_filtered = filtfilt(SOS, G, signal);
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fcutlow = 3000;
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fcuthigh = 3500;
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Wp = [fcutlow fcuthigh]/Fn; % Passband Frequency (Normalised)
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Ws = [fcutlow*0.95 fcuthigh/0.95]/Fn; % Stopband Frequency (Normalised)
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Rp = 30; % Passband Ripple (dB)
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Rs = 30; % Stopband Ripple (dB)
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%[n,Wn] = buttord(Wp,Ws,Rp,Rs); % Filter Order
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n=2;
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Wn=3200/Fn;
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[z,p,k] = butter(n, Wn, 'low'); % Filter Design
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[sosbp,gbp] = zp2sos(z, p, k); % Convert To Second-Order-Section For Stability
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%freqz(sosbp, 2^20, Fs) % Filter Bode Plot
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signal_filtered = filtfilt(sosbp, gbp, signal); % Filter Signal
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figure(2)
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subplot(2,1,1)
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plot(signal)
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subplot(2,1,2)
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plot(signal_filtered);
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% output
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audiowrite('restored.flac', signal_filtered, Fs); |