Signal Processing MATLAB Projects (25+ Ideas with Complete Code)

% Load or generate noisy ECG
x = ecg(5000);           % Built-in example or load real clinical data
t = (0:length(x)-1)/360; % fs = 360 Hz typical MIT-BIH rate

% Savitzky-Golay smoothing (preserves QRS peak morphology)
order = 3; framelen = 51;
y_sg = sgolayfilt(x, order, framelen);

% Zero-phase Butterworth low-pass filter (cutoff 40 Hz)
[b,a] = butter(4, 40/(360/2), 'low');
y_clean = filtfilt(b, a, y_sg);

figure; plot(t, x, 'b', t, y_clean, 'r', 'LineWidth', 1.5);
legend('Noisy Raw ECG', 'Filtered ECG'); xlabel('Time (s)'); ylabel('Amplitude');

fs = 1000; fp = 100; fs_stop = 150; Rp = 1; Rs = 60;

% 1. FIR Window Method (Kaiser)
N_fir = kaiserord([fp fs_stop]/(fs/2), [1 0], [0.01 60]);
b_fir = fir1(N_fir, fp/(fs/2), kaiser(N_fir+1));

% 2. IIR Butterworth Method
[N_iir, Wn] = buttord(fp/(fs/2), fs_stop/(fs/2), Rp, Rs);
[b_iir, a_iir] = butter(N_iir, Wn);

% Compare in Filter Visualization Tool
fvtool(b_fir, 1, b_iir, a_iir, 'Fs', fs); legend('FIR Kaiser', 'IIR Butterworth');

mu = 0.01; order = 32;
lms = dsp.LMSFilter('Length', order, 'StepSize', mu);

% Synthetic speech signal + noise
d = desired_signal;   % Primary microphone: speech + noise
x = reference_noise;  % Reference microphone: correlated noise
[y_est, e_clean] = lms(x, d); % e_clean = recovered speech

figure; plot(d, 'b'); hold on; plot(e_clean, 'r');
legend('Noisy Input', 'LMS Cleaned Output'); title('LMS Adaptive Noise Cancellation');

[audioIn, fs] = audioread('speech_sample.wav');

% 1. Estimate Pitch (F0)
winLen = round(0.03 * fs); overlap = round(0.02 * fs);
[pitchVal, ~] = pitch(audioIn, fs, 'Method', 'PEF', 'WindowLength', winLen, 'OverlapLength', overlap);

% 2. Formant Estimation using Linear Predictive Coding (LPC)
segment = audioIn(1:round(fs*0.1)); % Short voiced segment
A = lpc(segment, 12); rootsA = roots(A);
formants = sort(abs(rootsA(rootsA > 0 & imag(rootsA) > 0))) * (fs/(2*pi));
disp(['Formants F1-F3 (Hz): ', num2str(round(formants(1:3)'))]);

N = 64; CP = 16; M = 4; % 64 Subcarriers, QPSK
data = randi([0 M-1], N, 1);
modData = qammod(data, M, 'UnitAveragePower', true);

% IFFT Modulation + Cyclic Prefix
ifftSig = ifft(modData, N);
txSig = [ifftSig(end-CP+1:end); ifftSig];

% Pass through AWGN Channel
rxSig = awgn(txSig, 12, 'measured');

% Remove CP + FFT Demodulation
rxNoCP = rxSig(CP+1:end);
rxDemod = fft(rxNoCP, N);
rxBits = qamdemod(rxDemod, M, 'UnitAveragePower', true);
disp(['BER at 12 dB: ', num2str(mean(data ~= rxBits))]);

fs = 1e6; T = 10e-6; B = 100e3; % 10us duration, 100kHz bandwidth
t = 0:1/fs:T-1/fs;
pulse = chirp(t, 0, T, B);

% Simulate target echo at delay + noise
delay = round(2e-6 * fs);
echo = [zeros(1,delay) pulse zeros(1,500)];
echoNoisy = awgn(echo, 10, 'measured');

% Matched filter correlation
mf = fliplr(conj(pulse));
compressed = conv(echoNoisy, mf, 'same');
figure; plot(abs(compressed)); title('Radar Pulse Compression Output'); xlabel('Sample Index');

load eeg_example.mat; % Multi-channel clinical EEG matrix

% 1. Wavelet Denoising with SURE soft thresholding
level = 5; wname = 'db4';
denoised_ch1 = wdenoise(eegData(:,1), level, 'Wavelet', wname, ...
                        'DenoisingMethod', 'SURE', 'ThresholdRule', 'soft');

% 2. High-pass filter for slow baseline drift (< 0.5 Hz)
[b,a] = butter(4, 0.5/(fs/2), 'high');
cleaned = filtfilt(b, a, denoised_ch1);

figure; plot(eegData(:,1), 'b'); hold on; plot(cleaned, 'r');
legend('Raw EEG with Blink Artifact', 'Cleaned EEG Signal');