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

• Problem & Objective: Process noisy ECG signals by removing baseline wander, power-line interference (50/60 Hz), and high-frequency noise using low-pass, high-pass, notch, or Savitzky-Golay filters. Visualize raw vs. cleaned signals and detect R-peaks.
• Key MATLAB Functions: sgolayfilt, filter, filtfilt, designfilt
• Expected Output/Metrics: Filtered ECG plots, detected R-peak time locations, baseline wander suppression ratio, and SNR enhancement.

Sample Code Starter:

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

% Savitzky-Golay smoothing (good for preserving peaks)
order = 3; framelen = 51; % odd length
y = sgolayfilt(x, order, framelen);

% Simple low-pass (remove high-freq noise)
[b,a] = butter(4, 40/ (360/2), 'low'); % 40 Hz cutoff
y_lp = filtfilt(b, a, x);

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

• Problem & Objective: Design low-pass FIR (window/Kaiser) and IIR (Butterworth/Chebyshev/Elliptic) filters for identical specifications, comparing magnitude response, phase linearity, group delay, and filter order.
• Key MATLAB Functions: fir1, butter, cheby1, ellip, freqz, grpdelay, fvtool
• Expected Output/Metrics: Comparative magnitude/phase response curves, impulse/step response plots, filter order trade-offs, and stability assessment.

Sample Code Starter:

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

% FIR window method
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));

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

fvtool(b_fir, 1, b_iir, a_iir, 'Fs', fs); legend('FIR', 'IIR');

• Problem & Objective: Simulate amplitude modulation (AM) and frequency modulation (FM), pass signals through an AWGN channel, and recover baseband audio using envelope detection and PLL or frequency discriminator.
• Key MATLAB Functions: modulate, demodulate, awgn, hilbert
• Expected Output/Metrics: Time-domain modulated vs recovered waveforms, spectral density comparison, and demodulated SNR vs channel AWGN level curves.

• Problem & Objective: Compute real-time Fast Fourier Transform (FFT) on recorded or live audio streams to plot power spectral density, spectrograms, and identify dominant harmonic frequencies.
• Key MATLAB Functions: fft, spectrogram, pwelch, audioread, dsp.AudioRecorder
• Expected Output/Metrics: Real-time frequency spectrum display, spectrogram heatmap, dominant peak frequency tracking, and Total Harmonic Distortion (THD) metric.

• Problem & Objective: Decompose non-stationary audio and ECG signals using DWT (Haar, Daubechies), threshold wavelet coefficients (soft vs hard), and reconstruct noise-free signals.
• Key MATLAB Functions: dwt, wavedec, wdenoise, wdencmp
• Expected Output/Metrics: Multilevel wavelet decomposition plots, noise reduction factor, PSNR/SNR enhancement, and residual error waveform.

• Problem & Objective: Implement linear and circular convolution alongside cross-correlation and auto-correlation to measure time delays and extract periodic signals from random noise.
• Key MATLAB Functions: conv, xcorr, cconv
• Expected Output/Metrics: Convolution output waveforms, cross-correlation peak delay measurement, and signal detection probability in low-SNR environments.

• Problem & Objective: Demonstrate Nyquist-Shannon sampling theorem limits by sampling continuous sine and square waves above and below the Nyquist rate, visualizing frequency-domain aliasing folding.
• Key MATLAB Functions: sin, square, fft, resample
• Expected Output/Metrics: Time-domain sampled waveforms, FFT spectrum showing aliased spectral lines, and anti-aliasing low-pass filter performance.

• Problem & Objective: Map poles and zeros of discrete-time transfer functions onto the Z-plane to evaluate BIBO stability, region of convergence (ROC), and impulse/step response characteristics.
• Key MATLAB Functions: tf, zplane, impz, step, pzmap
• Expected Output/Metrics: Pole-zero constellation diagram, unit circle stability check, impulse response decay time, and transient step response metrics.

• Problem & Objective: Treat 2D grayscale images as spatial signals to perform spatial domain convolution, median/Gaussian noise filtering, and 2D gradient edge detection using Sobel and Prewitt operators.
• Key MATLAB Functions: imfilter, fspecial, edge, imread
• Expected Output/Metrics: Filtered image comparison, edge map binary masks, Mean Squared Error (MSE), and Structural Similarity Index (SSIM).

• Problem & Objective: Implement Least Mean Squares (LMS) adaptive filtering to suppress correlated interference from a primary speech signal using an auxiliary reference noise input.
• Key MATLAB Functions: dsp.LMSFilter, adaptfilt.lms
• Expected Output/Metrics: Convergence trajectory of filter weights, instantaneous squared error curve, audio noise reduction in dB, and frequency response adaptation.

Sample Code Starter:

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

d = desired_signal;   % noisy signal
x = reference_noise;  % correlated noise
[y, e] = lms(x, d);   % y = estimate of noise, e = cleaned signal

plot(d, 'b'); hold on; plot(e, 'r'); legend('Noisy', 'Cleaned');

• Problem & Objective: Extract fundamental frequency (pitch contour) using autocorrelation/cepstrum and estimate vocal tract formant frequencies via Linear Predictive Coding (LPC).
• Key MATLAB Functions: pitch, lpc, spectrogram, rceps
• Expected Output/Metrics: Pitch contour plot (Hz), LPC spectral envelope, estimated formant frequencies (F1, F2, F3), and voiced/unvoiced segmentation accuracy.

Sample Code Starter:

% Load speech audio
[audioIn, fs] = audioread('speech_sample.wav');  % or use your file

% Estimate pitch (fundamental frequency)
winLen = round(0.03 * fs);  % 30 ms window
overlap = round(0.02 * fs);
[pitchVal, ~] = pitch(audioIn, fs, 'Method', 'PEF', ...
                      'WindowLength', winLen, 'OverlapLength', overlap);

% Formant estimation using LPC (example on a short voiced segment)
segment = audioIn(1:fs*0.1);  % first 0.1s
order = 12;                   % typical for speech
A = lpc(segment, order);
rootsA = roots(A);
formants = sort(abs(rootsA(rootsA > 0 & imag(rootsA) > 0))) * (fs/(2*pi));

% Plot
t = (0:length(audioIn)-1)/fs;
subplot(2,1,1); plot(t, audioIn); title('Speech Signal');
subplot(2,1,2); plot(t(1:length(pitchVal)), pitchVal); title('Pitch Contour (Hz)');
disp('Estimated Formants (Hz):'); disp(formants(1:3));

• Problem & Objective: Simulate a complete 5G NR OFDM transceiver chain incorporating subcarrier mapping, IFFT/FFT, cyclic prefix insertion, multipath AWGN channel, and BER calculation for QPSK/16QAM.
• Key MATLAB Functions: ofdmmod, ofdmdemod, comm.AWGNChannel, qammod, qamdemod
• Expected Output/Metrics: BER vs SNR curves across modulation schemes, PAPR distribution plots, constellation diagrams, and spectral efficiency measurements.

Sample Code Starter:

% Basic OFDM parameters
N = 64;          % FFT size (subcarriers)
CP = 16;         % Cyclic prefix length
M = 4;           % QPSK
data = randi([0 M-1], N, 1);
modData = qammod(data, M, 'UnitAveragePower', true);

% IFFT + add CP
ifftSig = ifft(modData, N);
cpSig = [ifftSig(end-CP+1:end); ifftSig];

% Channel (simple AWGN)
snr = 10;  % dB
rxSig = awgn(cpSig, snr, 'measured');

% Remove CP + FFT
rxNoCP = rxSig(CP+1:end);
demodData = fft(rxNoCP, N);

% Demodulate and calculate BER
rxBits = qamdemod(demodData, M, 'UnitAveragePower', true);
ber = mean(data ~= rxBits);
disp(['BER at SNR = ' num2str(snr) ' dB: ' num2str(ber)]);

• Problem & Objective: Generate a linear frequency modulated (LFM/chirp) radar pulse, simulate noisy target echoes, and pass the received signal through a matched filter for pulse compression.
• Key MATLAB Functions: chirp, conv, xcorr, phased.LinearFMWaveform
• Expected Output/Metrics: Pulse compression mainlobe width, peak-to-sidelobe ratio (PSLR), target delay/range resolution improvement, and SNR gain.

Sample Code Starter:

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

% Simulate echo with delay and noise
delay = round(2e-6 * fs);          % target at 300 m range approx
echo = [zeros(1,delay) pulse zeros(1,500)];
echoNoisy = awgn(echo, 10, 'measured');

% Matched filter = time-reversed conjugate
mf = fliplr(conj(pulse));
compressed = conv(echoNoisy, mf, 'same');

% Plot
plot(abs(compressed)); title('Pulse Compressed Output'); xlabel('Samples');

• Problem & Objective: Eliminate ocular (eye blink), muscular, and power-line artifacts from multi-channel EEG recordings using stationary wavelet transform (SWT) or Independent Component Analysis (ICA).
• Key MATLAB Functions: wdenoise, eegfiltnew, butter, filtfilt
• Expected Output/Metrics: Cleaned vs raw multichannel EEG plots, power spectral density (PSD) preservation, artifact reduction ratio, and signal correlation coefficient.

Sample Code Starter:

% Assume eegData is your multi-channel EEG matrix
load eeg_example.mat;  % or use your data

% Wavelet denoising (example on channel 1)
level = 5; wname = 'db4';
denoised = wdenoise(eegData(:,1), level, 'Wavelet', wname, ...
                    'DenoisingMethod', 'SURE', 'ThresholdRule', 'soft');

% Simple high-pass to remove baseline
[b,a] = butter(4, 1/(fs/2), 'high');  % fs = sampling rate
cleaned = filtfilt(b, a, denoised);

plot(eegData(:,1), 'b'); hold on; plot(cleaned, 'r');
legend('Raw EEG', 'Artifact Removed');

• Problem & Objective: Detect fundamental frequencies in acoustic music recordings using STFT and autocorrelation peak detection, mapping detected pitches to standard musical notes (MIDI / A4=440Hz).
• Key MATLAB Functions: pitch, fft, findpeaks, audioread
• Expected Output/Metrics: Time-aligned pitch tracking contour, detected musical note sequence, frequency estimation accuracy (Cents error), and spectrogram representation.

Sample Code Starter:

[audio, fs] = audioread('music_clip.wav');
winLen = round(0.05 * fs);  % 50 ms window

% Pitch estimation over time
[pitchEst, time] = pitch(audio, fs, 'Method', 'SRH', ...
                         'WindowLength', winLen);

% Simple FFT-based single-note detection example
Y = fft(audio(1:winLen));
f = (0:winLen-1)*(fs/winLen);
[~, idx] = max(abs(Y(1:winLen/2)));
freq = f(idx);
note = 69 + 12 * log2(freq / 440);  % MIDI note number
disp(['Detected frequency: ' num2str(freq) ' Hz']);

• Problem & Objective: Model digital QPSK and 16-QAM modulators, transmit bits over AWGN and Rayleigh fading channels, and compare simulated Bit Error Rate (BER) against theoretical curves.
• Key MATLAB Functions: qammod, qamdemod, awgn, berawgn
• Expected Output/Metrics: Semi-log BER vs Eb/N0 curves, IQ constellation scatter plots, EVM (Error Vector Magnitude), and theoretical validation error.

Sample Code Starter:

M = 16;          % 16-QAM
data = randi([0 M-1], 10000, 1);
modSig = qammod(data, M, 'UnitAveragePower', true);

snr = 0:2:20;
ber = zeros(size(snr));
for i = 1:length(snr)
    rx = awgn(modSig, snr(i), 'measured');
    demod = qamdemod(rx, M, 'UnitAveragePower', true);
    ber(i) = mean(data ~= demod);
end

semilogy(snr, ber, 'b-o'); hold on;
semilogy(snr, berawgn(snr, 'qam', M), 'r--');
legend('Simulated', 'Theoretical'); xlabel('SNR (dB)'); ylabel('BER');

• Problem & Objective: Design low-pass, high-pass, and band-pass digital filters using Butterworth, Chebyshev Type I/II, and Elliptic approximations to satisfy tight passband ripple and stopband attenuation bounds.
• Key MATLAB Functions: butter, cheby1, cheby2, ellip, fvtool
• Expected Output/Metrics: Comparative magnitude/phase plots in fvtool, group delay comparison, filter order efficiency metrics, and transition band steepness.

Sample Code Starter:

[b_butt, a_butt] = butter(5, 0.2);          % order 5, cutoff 0.2*pi
[b_cheb1, a_cheb1] = cheby1(5, 1, 0.2);        % 1 dB ripple

fvtool(b_butt, a_butt, b_cheb1, a_cheb1);
legend('Butterworth', 'Chebyshev Type I');

• Problem & Objective: Build an interactive 5-band parametric graphic equalizer GUI with adjustable gain, center frequency, and Q-factor sliders for real-time audio playback filtering.
• Key MATLAB Functions: dsp.ParametricEQFilter, dsp.AudioPlayer, audioread, uifigure
• Expected Output/Metrics: Real-time frequency response curve update, processed audio playback quality, gain adjustment range (-12dB to +12dB), and latency measurements.

• Problem & Objective: Apply 2D Discrete Wavelet Transform (2D DWT) to digital images, perform coefficient quantization/thresholding, and evaluate trade-offs between compression ratio and image quality.
• Key MATLAB Functions: dwt2, idwt2, wthresh, wavedec2, psnr
• Expected Output/Metrics: Reconstructed image montage, Compression Ratio (CR), Peak Signal-to-Noise Ratio (PSNR in dB), and Structural Similarity Index (SSIM).

Sample Code Starter:

I = imread('cameraman.tif'); level = 2; wname = 'haar';
[C, S] = wavedec2(I, level, wname);

% Threshold small coefficients
Cthresh = wthresh(C, 'h', 30);  % hard threshold example

Icomp = waverec2(Cthresh, S, wname);
imshowpair(I, uint8(Icomp), 'montage');
psnrVal = psnr(Icomp, double(I));
disp(['PSNR: ' num2str(psnrVal) ' dB']);

• Problem & Objective: Simulate a 2x2 spatial multiplexing MIMO transmission link over Rayleigh fading, applying Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) equalization at the receiver.
• Key MATLAB Functions: comm.MIMOChannel, comm.RayleighChannel, qammod, qamdemod
• Expected Output/Metrics: BER vs SNR curves for ZF vs MMSE equalizers, spatial diversity gain comparison, channel capacity estimations, and constellation plots.

Sample Code Starter:

% Simple 2x2 MIMO example
Nt = 2; Nr = 2; M = 4;
data = randi([0 M-1], 1000, Nt);
txSig = qammod(data, M);

chan = comm.MIMOChannel('SampleRate', 1e6, 'PathDelays', 0, ...
                        'AveragePathGains', 0, 'MaximumDopplerShift', 5);

rxSig = chan(txSig);
snr = 15;
rxNoisy = awgn(rxSig, snr, 'measured');

% Zero-forcing equalization (simple pseudo-inverse)
Hest = eye(Nr,Nt);  % assume perfect CSI
equalized = (Hest \ rxNoisy.')';

• Problem & Objective: Analyze nasal airflow and chest movement respiration signals to detect sleep apnea events (respiratory pauses > 10s) using moving RMS envelopes and peak detection.
• Key MATLAB Functions: findpeaks, movmean, envelope
• Expected Output/Metrics: Respiration envelope plots, apnea event start/end timestamps, apnea-hypopnea index (AHI) score, and detection sensitivity/specificity.

Sample Code Starter:

% Assume resp_signal is your breathing waveform, fs = sampling rate
load breathing_data.mat;  % or use your file
t = (0:length(resp_signal)-1)/fs;

% Smooth and find envelope
env = envelope(resp_signal, round(0.5*fs), 'rms');  % 0.5s window
smooth_env = movmean(env, round(2*fs));             % 2s moving average

% Detect apnea: periods where envelope drops below threshold
thresh = 0.3 * mean(smooth_env);
apnea_mask = smooth_env < thresh;

% Find start/end of apnea events (>10s)
[pks, locs] = findpeaks(double(~apnea_mask), 'MinPeakDistance', fs*5);
apnea_starts = locs(diff([0; locs]) > fs*10);  % events >10s

% Plot
plot(t, resp_signal, 'b'); hold on;
plot(t, smooth_env, 'g', 'LineWidth', 2);
plot(t(apnea_mask), resp_signal(apnea_mask), 'r.', 'MarkerSize', 10);
title('Breathing Signal with Detected Apnea Events');
legend('Raw Signal', 'Envelope', 'Apnea Regions');

• Problem & Objective: Model Sinusoidal PWM (SPWM), Third Harmonic Injection PWM (THIPWM), and Space Vector PWM (SVPWM) in Simulink to compare DC bus utilization and Total Harmonic Distortion (THD).
• Key MATLAB Functions: sin, fft, powergui, power_fftscope
• Expected Output/Metrics: Voltage/current output waveforms, fundamental magnitude comparison, harmonic spectrum plots, and THD percentage metrics.

Sample Code Starter (MATLAB script to generate SPWM reference):

fs = 10000;           % Switching frequency
fm = 50;              % Fundamental frequency
ma = 0.9;             % Modulation index
t = 0:1/fs:0.02;

% Sinusoidal PWM reference
ref_sin = ma * sin(2*pi*fm*t);

% Third Harmonic Injection
ref_thi = ref_sin + (1/6) * sin(6*pi*fm*t);

% Plot references
plot(t*1000, ref_sin, 'b', t*1000, ref_thi, 'r--', 'LineWidth', 1.5);
legend('Sinusoidal PWM', 'Third Harmonic Injection');
xlabel('Time (ms)'); ylabel('Normalized Reference');
title('PWM Reference Signals Comparison');

• Problem & Objective: Process 2D/3D LiDAR range scans by filtering noise, segmenting ground planes, clustering obstacle points, and mapping spatial coordinates for autonomous collision avoidance.
• Key MATLAB Functions: pcdenoise, findpeaks, polarplot, lidarPointCloud
• Expected Output/Metrics: Polar LiDAR range scan plots, detected obstacle cluster distances/angles, false alarm rate, and processing execution time.

Sample Code Starter:

% Simulated LiDAR scan: angles and ranges
theta = linspace(-pi, pi, 360);   % 1-degree resolution
ranges = 5 + 2*randn(size(theta));  % background at ~5m
ranges(100:150) = 1.5;              % obstacle at 1.5m in sector

% Simple threshold-based detection
obstacle_mask = ranges < 3;         % closer than 3m

% Find clusters (basic)
[~, locs] = findpeaks(-ranges, 'MinPeakProminence', 1);

% Plot polar
figure; polarplot(theta, ranges, 'b');
hold on; polarplot(theta(obstacle_mask), ranges(obstacle_mask), 'r.');
title('LiDAR Scan - Obstacle Detection');
legend('All Points', 'Detected Obstacles');

• Problem & Objective: Separate overlapping frequency-division or time-frequency signals at a wireless receiver using multi-level DWT sub-band decomposition and wave-reconstruction filters.
• Key MATLAB Functions: wavedec, waverec, appcoef, detcoef, wrcoef
• Expected Output/Metrics: Decomposed sub-band signal plots, recovered individual signal waveforms, Signal-to-Interference Ratio (SIR) improvement, and reconstruction error.

Sample Code Starter:

% Superimposed signals example
t = 0:0.001:1;
s1 = sin(2*pi*50*t);     % low freq
s2 = 0.5*sin(2*pi*200*t); % high freq
x = s1 + s2 + 0.2*randn(size(t));

% Multi-level DWT
[c, l] = wavedec(x, 5, 'db4');

% Approximate coefficients (low freq) → s1
a5 = appcoef(c, l, 'db4', 5);
s1_rec = wrcoef('a', c, l, 'db4', 5);

% Detail coefficients at certain level → s2 approximation
d3 = detcoef(c, l, 3);
s2_rec = wrcoef('d', c, l, 'db4', 3);

% Plot
subplot(3,1,1); plot(t, x); title('Composite Signal');
subplot(3,1,2); plot(t, s1_rec); title('Recovered Low-Freq Signal');
subplot(3,1,3); plot(t, s2_rec); title('Recovered High-Freq Component');

• Problem & Objective: Analyze quantization noise, bit overflow, and dynamic range trade-offs when converting floating-point DSP algorithms (FIR filters, FFT) to fixed-point hardware architectures.
• Key MATLAB Functions: fi, numerictype, fimath, filter
• Expected Output/Metrics: Fixed-point vs floating-point BER overlay, quantization noise variance curve, bit-width optimization profile, and EVM degradation analysis.

Sample Code Starter:

% Fixed-point FIR filter example
wl = 16; fl = 10;           % word length, fraction length
x = fi(randn(1, 5000), 1, wl, fl);   % signed fixed-point input

% Filter coefficients in fixed-point
b = fi([0.05 0.15 0.6 0.15 0.05], 1, wl, fl);

% Fixed-point filtering (using filter with fi objects)
y_fixed = filter(double(b), 1, double(x));   % simulate fixed behavior

% Floating-point reference
y_float = filter(double(b), 1, double(x));

% Quantization error
error = double(y_fixed) - y_float;

% Plot
subplot(2,1,1); plot(double(y_fixed), 'b'); hold on; plot(y_float, 'r--');
legend('Fixed-Point', 'Floating-Point');
subplot(2,1,2); plot(error); title('Quantization Error');