sem2 done
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Priec
198
semestralka2/final.m
Normal file
198
semestralka2/final.m
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clc;
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close all;
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clear;
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%% === LOAD AUDIO FILES ===
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[y1, Fs1] = audioread('flac.wav');
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[y2, Fs2] = audioread('flac2.wav');
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if size(y1,2) > 1, y1 = mean(y1,2); end
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if size(y2,2) > 1, y2 = mean(y2,2); end
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t1 = (0:length(y1)-1) / Fs1;
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t2 = (0:length(y2)-1) / Fs2;
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fprintf('Loaded flac.wav: %d samples, Fs = %d Hz\n', length(y1), Fs1);
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fprintf('Loaded flac2.wav: %d samples, Fs = %d Hz\n', length(y2), Fs2);
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%% === TIME-DOMAIN WAVEFORMS ===
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figure;
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subplot(2,1,1);
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plot(t1, y1);
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xlabel('Time [s]');
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ylabel('Amplitude');
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title('flac.wav – Time Domain');
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grid on;
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subplot(2,1,2);
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plot(t2, y2, 'r');
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xlabel('Time [s]');
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ylabel('Amplitude');
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title('flac2.wav – Time Domain');
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grid on;
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%% === FREQUENCY-DOMAIN ANALYSIS (FFT) ===
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N1 = length(y1);
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N2 = length(y2);
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X1 = fft(y1);
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X2 = fft(y2);
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freq_shift1 = (-N1/2 : N1/2 - 1) * (Fs1 / N1);
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freq_shift2 = (-N2/2 : N2/2 - 1) * (Fs2 / N2);
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% --- Real / Imag components ---
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figure;
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subplot(2,1,1);
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plot(freq_shift2, real(fftshift(X2)));
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xlabel('Frequency [Hz]');
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ylabel('Real Part');
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title('flac2.wav – Real Part of FFT');
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grid on;
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subplot(2,1,2);
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plot(freq_shift2, imag(fftshift(X2)), 'r');
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xlabel('Frequency [Hz]');
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ylabel('Imag Part');
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title('flac2.wav – Imaginary Part of FFT');
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grid on;
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% --- Magnitude Spectrum ---
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figure;
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plot(freq_shift2, 20*log10(abs(fftshift(X2)) + eps), 'y');
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xlabel('Frequency [Hz]');
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ylabel('Magnitude [dB]');
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title('flac2.wav – Magnitude Spectrum');
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xlim([0 2000]);
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grid on;
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%% === PEAK DETECTION TO FIND TONAL NOISE FREQUENCIES ===
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halfN2 = floor(N2/2);
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f2 = (0:halfN2-1)*(Fs2/N2);
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mag2 = abs(X2(1:halfN2));
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[pks, locs] = findpeaks(mag2, 'MinPeakHeight', max(mag2)*0.1, ...
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'MinPeakDistance', round(10*N2/Fs2));
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f_tonal = f2(locs);
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f_tonal = f_tonal(f_tonal > 30 & f_tonal < Fs2/2);
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f_tonal = sort(f_tonal(1:min(5, numel(f_tonal))));
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fprintf('\nDetected tonal noise frequencies (from flac2.wav):\n');
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fprintf(' %.1f Hz\n', f_tonal);
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%% === NOTCH FILTER DESIGN (Z-domain zeros/poles) ===
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r = 0.995; % pole radius
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sos_all = [];
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g_all = 1;
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for i = 1:length(f_tonal)
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f0 = f_tonal(i);
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w0 = 2*pi*f0/Fs1; % use Fs1 so it matches flac.wav
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z = [exp(1j*w0) exp(-1j*w0)]; % zeros at ±ω0
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p = r*z; % poles slightly inside circle
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b = real(poly(z));
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a = real(poly(p));
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b = b / (sum(b)/sum(a)); % normalize gain
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[sos_i, g_i] = tf2sos(b, a);
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sos_all = [sos_all; sos_i];
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g_all = g_all * g_i;
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end
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%% === VISUALIZE NOTCH FILTER RESPONSE ===
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figure;
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freqz(sos_all, 2048, Fs1);
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title('Notch Filter Cascade – Based on flac2 Noise Frequencies');
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%% === APPLY NOTCH FILTERS TO SIGNALS ===
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y1_filtered = filtfilt(sos_all, g_all, y1); % Apply to flac.wav
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y2_filtered = filtfilt(sos_all, g_all, y2); % Apply to flac2.wav
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y1_filtered = y1_filtered / max(abs(y1_filtered));
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y2_filtered = y2_filtered / max(abs(y2_filtered));
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audiowrite('filtered_flac.wav', y1_filtered, Fs1);
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audiowrite('filtered_flac2.wav', y2_filtered, Fs2);
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fprintf('\nSaved:\n');
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fprintf(' filtered_flac.wav (filtered signal)\n');
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fprintf(' filtered_flac2.wav (filtered noise sample)\n');
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%% === COMPARE ORIGINAL VS FILTERED SPECTRA ===
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N_plot = length(y1);
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f_plot = (0:N_plot/2-1) * Fs1 / N_plot;
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X1_orig = fft(y1);
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X1_filt = fft(y1_filtered);
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figure;
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subplot(2,1,1);
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plot(f_plot, 20*log10(abs(X1_orig(1:N_plot/2))+eps));
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xlabel('Frequency [Hz]'); ylabel('Magnitude [dB]');
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title('flac.wav – Original Spectrum');
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xlim([0 2000]); grid on;
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subplot(2,1,2);
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plot(f_plot, 20*log10(abs(X1_filt(1:N_plot/2))+eps), 'r');
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xlabel('Frequency [Hz]'); ylabel('Magnitude [dB]');
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title('flac.wav – After Notch Filtering');
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xlim([0 2000]); grid on;
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%% === APPLY THE SAME FILTER TO sem2.wav ===
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fprintf('\n=== APPLYING DESIGNED FILTER TO sem2.wav ===\n');
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[y_sem2, Fs_sem2] = audioread('sem2.wav');
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if size(y_sem2,2) > 1, y_sem2 = mean(y_sem2,2); end
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if Fs_sem2 ~= Fs1
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warning('Sample rates differ – resampling sem2.wav to match notch filter design');
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y_sem2 = resample(y_sem2, Fs1, Fs_sem2);
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Fs_sem2 = Fs1;
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end
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% --- Step 1: Apply multi‑notch filter from flac2 noise analysis ---
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y_sem2_notch = filtfilt(sos_all, g_all, y_sem2);
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% --- Step 2 (optional): add band‑pass filter for speech 100‑8000 Hz ---
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[z_bp, p_bp, k_bp] = butter(4, [100 8000]/(Fs_sem2/2), 'bandpass');
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[sos_bp, g_bp] = zp2sos(z_bp, p_bp, k_bp);
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y_sem2_final = filtfilt(sos_bp, g_bp, y_sem2_notch);
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% --- Normalize both versions ---
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y_sem2_notch = y_sem2_notch / (max(abs(y_sem2_notch)) + eps) * 0.95;
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y_sem2_final = y_sem2_final / (max(abs(y_sem2_final)) + eps) * 0.95;
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% --- Save results ---
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audiowrite('sem2_notch_filtered.wav', y_sem2_notch, Fs_sem2);
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audiowrite('sem2_final.wav', y_sem2_final, Fs_sem2);
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fprintf('Saved:\n');
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fprintf(' sem2_notch_filtered.wav (notch filters only)\n');
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fprintf(' sem2_final.wav (notch + speech band‑pass)\n');
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%% === VISUAL CHECK – BEFORE vs AFTER ===
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t_sem2 = (0:length(y_sem2)-1)/Fs_sem2;
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N_sem2 = length(y_sem2);
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f_sem2 = (0:N_sem2/2-1)*Fs_sem2/N_sem2;
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Y_orig = fft(y_sem2);
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Y_filt = fft(y_sem2_final);
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figure;
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subplot(2,1,1);
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plot(t_sem2, y_sem2);
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xlabel('Time [s]'); ylabel('Amplitude');
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title('sem2.wav – Original Time Domain'); grid on;
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subplot(2,1,2);
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plot(t_sem2, y_sem2_final, 'r');
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xlabel('Time [s]'); ylabel('Amplitude');
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title('sem2\_final.wav – After Notch + Band‑Pass'); grid on;
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figure;
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subplot(2,1,1);
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plot(f_sem2, 20*log10(abs(Y_orig(1:N_sem2/2))+eps));
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xlabel('Frequency [Hz]'); ylabel('Magnitude [dB]');
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title('sem2.wav – Original Spectrum');
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xlim([0 2000]); grid on;
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subplot(2,1,2);
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plot(f_sem2, 20*log10(abs(Y_filt(1:N_sem2/2))+eps), 'r');
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xlabel('Frequency [Hz]'); ylabel('Magnitude [dB]');
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title('sem2\_final.wav – After Filtering');
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xlim([0 2000]); grid on;
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