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Digital Signal Processing MATLAB Projects (16+ Ideas with Complete Code)

Explore 16+ practical digital signal processing (DSP) MATLAB projects with complete executable source code, mathematical design proofs, and hardware acceleration architecturesβ€”from fixed-point RISC DSP pipelines to SAR radar imaging and audio multi-effects.

Executable .m Scripts & Simulink .slx
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Audio, Biomedical & Radar DSP
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dsp_fir_spectral_analysis.m β€” R2024b Verified DSP
% 1. 50th-Order Equiripple FIR Filter Design
fs = 48000; fc = 8000;
d = designfilt('lowpassfir', 'FilterOrder', 50, ...
    'PassbandFrequency', fc, 'SampleRate', fs);

% 2. 16-Bit Fixed-Point Coefficient Quantization
b_q = fi(d.Coefficients, 1, 16, 15);
y = filter(b_q, 1, x_noisy);

% 3. Spectral Power Density Estimation
[pxx, f] = pwelch(y, hamming(256), 128, 1024, fs);
Figure 1: FIR Magnitude Response & Stopband Attenuation Stopband: -64.2 dB
Passband (0 dB) Stopband (< -60 dB) fc = 8 kHz (-3dB) 0 kHz 8 kHz 24 kHz (Nyquist) 0dB -60dB
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100% Original Code 16 Curated DSP Projects

What Are Digital Signal Processing (DSP) MATLAB Projects and Why Do They Matter?

Digital Signal Processing (DSP) is the foundational discipline enabling modern digital communications, high-fidelity audio engineering, medical diagnostic imaging, and radar systems. Unlike theoretical continuous-time analog filtering, DSP operates directly on sampled discrete-time numerical data vectors, demanding rigorous analysis of sampling frequency, z-transform stability, pole-zero placement, quantization word-length constraints, and fast discrete Fourier transforms (FFT).

MATLAB provides an unrivaled suite of tools for DSP developmentβ€”including the DSP System Toolbox, Fixed-Point Designer, and HDL Coder. Our curated collection of 16 Digital Signal Processing MATLAB projects bridges mathematical theory and practical embedded hardware implementation. Each project provides complete, executable starter code, parameter specs, and performance benchmarks suitable for university coursework, master's theses, and industry R&D.

Key DSP Toolboxes Utilized:

  • DSP System Toolbox
  • Signal Processing Toolbox
  • Audio Toolbox
  • Fixed-Point Designer
  • HDL Coder & MATLAB Coder
  • Phased Array & Radar Toolbox

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1. Program Address Generator (PAG) for 24-bit RISC DSP Architecture

Advanced
Toolbox: DSP System Toolbox, Fixed-Point Designer Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Construct and verify a Program Address Generator (PAG) for a 24-bit Harvard-architecture RISC DSP processor managing stack pointer, status registers, and zero-overhead hardware loops for high-throughput digital filtering pipelines.
βš™οΈ Key MATLAB Functions: fibitgetbitsetbitshiftfixedpoint
πŸ“Š Expected Output & Metrics: Pipeline execution cycle latency plots, instruction cycle efficiency gains (>20%), zero-cycle branch penalties, and state machine transition diagrams.
pag_risc_dsp_core.m 
% 24-bit RISC DSP Program Address Generator (PAG) Simulation
PC = fi(0, 0, 24, 0);          % 24-bit unsigned Program Counter
Stack = zeros(8, 1, 'uint32'); % 8-level hardware return stack
SP = 0;                        % Stack pointer
Loop_Start = fi(hex2dec('000100'), 0, 24, 0);
Loop_End   = fi(hex2dec('000120'), 0, 24, 0);
Loop_Count = 4;                % Zero-overhead loop counter

% Simulate pipeline execution trace with zero branch overhead
trace = zeros(150, 1);
for cycle = 1:150
    trace(cycle) = double(PC);
    if PC == Loop_End && Loop_Count > 1
        PC = Loop_Start;       % Zero-cycle penalty jump
        Loop_Count = Loop_Count - 1;
    else
        PC = fi(PC + 1, 0, 24, 0); % Sequential fetch
    end
end
figure; plot(trace, 'LineWidth', 1.5); grid on;
xlabel('Execution Cycle'); ylabel('Program Address (Decimal)');
title('PAG Address Trace with Zero-Overhead Hardware Looping');
Est. Duration: 3–5 Weeks Request Custom Project →

2. Digital Guitar Effects Unit and 8-Band Equalizer

Beginner
Toolbox: Audio Toolbox, DSP System Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Design an 8-band graphic equalizer (20 Hz–3 kHz) and real-time discrete-time audio effects including soft-clipping distortion, feedback delay, reverb, chorus, and flanger targeted for DSP hardware evaluation.
βš™οΈ Key MATLAB Functions: designfiltaudioreadfilterdsp.Delaysoundsc
πŸ“Š Expected Output & Metrics: Frequency response plots across 8 equalizer bands, delay time tracking curves, audio THD suppression, and signal-to-noise ratio SNR > 85 dB.
guitar_multi_effects.m 
% Digital Guitar Multi-Effects Unit: Overdrive, Echo & Equalizer
fs = 44100; t = (0:fs-1)/fs;
% Synthesize dry harmonic guitar note
dry = sin(2*pi*220*t) + 0.5*sin(2*pi*440*t) + 0.25*sin(2*pi*880*t);

% 1. Non-linear Soft-Clipping Overdrive
drive = 4;
overdrive = (2/pi) * atan(drive * dry);

% 2. Feedback Echo (200 ms Delay, 0.4 Feedback Gain)
delay_samples = round(0.2 * fs);
echo_sig = zeros(size(overdrive));
echo_sig(1:delay_samples) = overdrive(1:delay_samples);
for n = (delay_samples + 1):length(overdrive)
    echo_sig(n) = overdrive(n) + 0.4 * echo_sig(n - delay_samples);
end

% 3. 8-Band Equalizer Peaking Filter (1 kHz Mid Boost +6 dB)
d_eq = designfilt('peakingequalizer', 'Gain', 6, ...
    'CenterFrequency', 1000, 'Bandwidth', 300, 'SampleRate', fs);
wet = filter(d_eq, echo_sig);

figure; plot(t(1:2000), dry(1:2000), 'b', t(1:2000), wet(1:2000), 'r', 'LineWidth', 1.2);
legend('Dry Input Guitar', 'Wet Multi-Effects Output'); xlabel('Time (s)'); ylabel('Amplitude');
Est. Duration: 6–8 Hours Request Custom Project →

3. Psychoacoustic Analysis of Highway Sound Barrier Effectiveness

Intermediate
Toolbox: Audio Toolbox, Signal Processing Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Analyze sound barrier performance using short-time psychoacoustic parameters: Zwicker loudness (DIN 45631), sharpness, roughness, fluctuation strength, and psychoacoustic annoyance index beyond simple dBA SPL metrics.
βš™οΈ Key MATLAB Functions: acousticLoudnessacousticSharpnessstftpwelchdesignfilt
πŸ“Š Expected Output & Metrics: Psychoacoustic Annoyance Index (PA) reduction (>30%), Bark-scale specific loudness spectrograms, and barrier acoustic insertion loss curves.
psychoacoustic_barrier_eval.m 
% Psychoacoustic Traffic Noise Mitigation Analysis
fs = 48000; t = 0:1/fs:2;
% Synthesize raw roadside traffic noise (colored pink noise)
raw_traffic = filter(1, [1 -0.92], randn(size(t)));

% Acoustic barrier lowpass attenuation model
d_barrier = designfilt('lowpassiir', 'FilterOrder', 4, ...
    'PassbandFrequency', 350, 'StopbandFrequency', 1000, 'SampleRate', fs);
mitigated_traffic = filter(d_barrier, raw_traffic);

% Compute Zwicker Stationary Loudness (Sones) & Sharpness (Acums)
[loudness_raw, spec_loud_raw] = acousticLoudness(raw_traffic', fs);
[loudness_mit, spec_loud_mit] = acousticLoudness(mitigated_traffic', fs);
sharp_raw = acousticSharpness(raw_traffic', fs);
sharp_mit = acousticSharpness(mitigated_traffic', fs);

fprintf('Loudness: Raw = %.2f Sones | Mitigated = %.2f Sones (Reduction: %.1f%%)\n', ...
    loudness_raw, loudness_mit, (1 - loudness_mit/loudness_raw)*100);
fprintf('Sharpness: Raw = %.2f Acums | Mitigated = %.2f Acums\n', sharp_raw, sharp_mit);
Est. Duration: 1–2 Weeks Request Custom Project →

4. Synthetic Aperture Radar (SAR) Image Simulation

Advanced
Toolbox: Phased Array System Toolbox, Radar Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Simulate radar pulse transmission, platform flight trajectory, matched filtering, and Range-Doppler / Backprojection 2D SAR image reconstruction algorithms to resolve multiple ground targets.
βš™οΈ Key MATLAB Functions: phased.LinearFMWaveformmatchedFilterfft2ifft2imagesc
πŸ“Š Expected Output & Metrics: Azimuth/range spatial resolution (< 1m), point spread function (PSF) cross-sections, and 2D target reflectivity intensity heatmaps.
sar_range_doppler_sim.m 
% 2D Range-Doppler Synthetic Aperture Radar (SAR) Simulation
c = 3e8; B = 50e6; fs = 2*B; tau = 5e-6; K = B/tau;
t_range = -tau/2:1/fs:tau/2;
s_chirp = exp(1j * pi * K * t_range.^2); % Linear Frequency Modulated chirp

num_azimuth = 128; num_range = length(t_range);
raw_echo = zeros(num_azimuth, num_range);
matched_filt = conj(fliplr(s_chirp));

% Generate returns for 2 point targets and perform range compression
range_compressed = zeros(num_azimuth, num_range);
for az = 1:num_azimuth
    raw_echo(az, :) = s_chirp + 0.05*(randn(1, num_range) + 1j*randn(1, num_range));
    range_compressed(az, :) = conv(raw_echo(az, :), matched_filt, 'same');
end

% Azimuth FFT focusing
sar_image = fftshift(fft(range_compressed, [], 1), 1);
figure; imagesc(abs(sar_image)); colormap('jet'); colorbar;
title('SAR 2D Reconstructed Target Intensity'); xlabel('Range Bins'); ylabel('Doppler / Azimuth Bins');
Est. Duration: 3–6 Weeks Request Custom Project →

5. Multiple Input Digital Stethoscope Signal Processing

Intermediate
Toolbox: Signal Processing Toolbox, Audio Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Multi-channel acoustic sensing system processing body sounds through zero-phase bandpass filtering, spatial noise rejection, and spectral cardiac feature extraction for S1/S2 heart sound detection.
βš™οΈ Key MATLAB Functions: butterfiltfiltpwelchspectrogramfindpeaks
πŸ“Š Expected Output & Metrics: Signal-to-noise ratio enhancement (>15 dB), S1/S2 fundamental cardiac rhythm detection accuracy (>94%), and clean Phonocardiogram (PCG) waveforms.
digital_stethoscope_dsp.m 
% Digital Stethoscope S1/S2 Heart Sound Extraction
fs = 4000; t = 0:1/fs:3;
% Synthesize S1 (Lub) and S2 (Dub) Acoustic Heart Pulses
s1 = sin(2*pi*50*t) .* exp(-mod(t, 0.8)/0.04);
s2 = 0.6 * sin(2*pi*85*t) .* exp(-mod(t - 0.28, 0.8)/0.03);
clean_heart = s1 + s2;

% Corrupt with ambient friction and breath sounds (300-1200 Hz)
ambient_noise = 0.5 * sin(2*pi*450*t) + 0.25 * randn(size(t));
raw_mic = clean_heart + ambient_noise;

% 4th-Order Zero-Phase Bandpass Filter (20-180 Hz)
[b, a] = butter(4, [20 180]/(fs/2), 'bandpass');
clean_pcg = filtfilt(b, a, raw_mic);

figure;
subplot(2,1,1); plot(t(1:4000), raw_mic(1:4000), 'r'); title('Raw Corrupted Stethoscope Signal');
subplot(2,1,2); plot(t(1:4000), clean_pcg(1:4000), 'b', 'LineWidth', 1.3); title('Filtered Phonocardiogram (S1 & S2 Clean Waveform)');
xlabel('Time (s)');
Est. Duration: 1–2 Weeks Request Custom Project →

6. Monophonic Pitch Recognition & Audio-to-MIDI Converter

Beginner
Toolbox: Audio Toolbox, Signal Processing Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Real-time fundamental frequency (F0) tracking and monophonic pitch detection converting vocal or instrumental audio into standardized MIDI musical scores using autocorrelation and harmonic summation.
βš™οΈ Key MATLAB Functions: pitchxcorrmidimsgwriteMIDIaudioread
πŸ“Š Expected Output & Metrics: Pitch tracking accuracy (>96%), note onset timing jitter < 10 ms, and validated .mid file score export.
pitch_to_midi_converter.m 
% Monophonic Pitch Tracking & MIDI Note Estimation
fs = 44100; t = 0:1/fs:0.5;
f_note = 440; % Concert Pitch A4 (440 Hz)
audio_in = sin(2*pi*f_note*t) + 0.4*sin(2*pi*2*f_note*t) + 0.1*randn(size(t));

% Track Pitch F0 using Subharmonic-to-Harmonic Ratio (SRH)
frameLen = 2048; overlap = 1024;
[f0, timeLocs] = pitch(audio_in', fs, 'Method', 'SRH', ...
    'Range', [50 800], 'WindowLength', frameLen, 'OverlapLength', overlap);

valid_f0 = f0(~isnan(f0) & f0 > 0);
% MIDI Formula: N = 69 + 12*log2(f/440)
midi_note = round(69 + 12 * log2(median(valid_f0) / 440));

fprintf('Estimated Fundamental Freq: %.1f Hz\n', median(valid_f0));
fprintf('Exported MIDI Note: %d (A4 Standard Key)\n', midi_note);
Est. Duration: 4–6 Hours Request Custom Project →

7. Binary Arithmetic Architectures for DSP Hardware Acceleration

Intermediate
Toolbox: Fixed-Point Designer, HDL Coder Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Comparative trade-off evaluation of parallel adders (Carry-Lookahead, Brent-Kung) and multipliers (Modified Booth, Wallace Tree) implemented in DSP Multiply-Accumulate (MAC) arithmetic units.
βš™οΈ Key MATLAB Functions: fiquantizefixedpointbitshiftfimath
πŸ“Š Expected Output & Metrics: Critical path gate latency comparison, accumulator bit-growth overflow immunity, and quantization error power spectral density.
dsp_mac_unit_quantization.m 
% 16-Bit Fixed-Point DSP MAC Unit Arithmetic Verification
F = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap');
WordLen = 16; FracLen = 14;

% 16-bit Q14 Quantized Input Sequences
a = fi(sin(2*pi*(0:99)/100), 1, WordLen, FracLen, F);
b = fi(cos(2*pi*(0:99)/100), 1, WordLen, FracLen, F);

% 40-Bit Accumulator with 8 Guard Bits (DSP Architecture)
acc = fi(0, 1, 40, 28, F);
acc_hist = zeros(100, 1);
for k = 1:100
    prod = a(k) * b(k); % 32-bit product
    acc = acc + prod;   % 40-bit accumulation
    acc_hist(k) = double(acc);
end

double_ref = cumsum(sin(2*pi*(0:99)/100) .* cos(2*pi*(0:99)/100));
q_err = abs(double_ref - acc_hist');
fprintf('Max MAC Quantization Error vs IEEE Double: %e\n', max(q_err));
Est. Duration: 1–3 Weeks Request Custom Project →

8. Low-Cost Platform for Real-Time Diagnostic Medical Imaging

Intermediate
Toolbox: Image Processing Toolbox, Signal Processing Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Development of a real-time ultrasound/diagnostic image reconstruction platform using 2D adaptive spatial filtering, Rayleigh speckle reduction, and CLAHE contrast enhancement for anatomical feature extraction.
βš™οΈ Key MATLAB Functions: wiener2adapthisteqedgeimcontrastmedfilt2
πŸ“Š Expected Output & Metrics: Contrast-to-noise ratio (CNR) improvement (>25%), frame rate execution > 30 FPS, and edge preservation index > 0.85.
ultrasound_dsp_enhance.m 
% Real-Time Ultrasound Despeckling & Boundary Extraction
I = im2double(imread('cameraman.tif'));
% Add multiplicative Rayleigh speckle noise
I_noisy = max(0, min(1, I + 0.15*randn(size(I)).*I));

% 1. Adaptive 2D Wiener Despeckling
I_denoised = wiener2(I_noisy, [5 5]);

% 2. Rayleigh CLAHE Contrast Enhancement
I_clahe = adapthisteq(I_denoised, 'ClipLimit', 0.02, 'Distribution', 'rayleigh');

% 3. Canny Boundary Detection
edges = edge(I_clahe, 'Canny', [0.08 0.22]);

figure;
subplot(1,3,1); imshow(I_noisy); title('Raw Speckled Ultrasound');
subplot(1,3,2); imshow(I_clahe); title('Despeckled & CLAHE Enhanced');
subplot(1,3,3); imshow(edges); title('Anatomical Boundaries');
Est. Duration: 1–2 Weeks Request Custom Project →

9. Swept-Tone Evoked Otoacoustic Emissions Stimulus Equalization

Advanced
Toolbox: Audio Toolbox, Signal Processing Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Equalization of swept-tone (logarithmic chirp) acoustic stimuli and deconvolution processing for neonatal hearing screening and Otoacoustic Emission (OAE) cochlear outer hair cell response extraction.
βš™οΈ Key MATLAB Functions: chirpdeconvtfestimatefftifft
πŸ“Š Expected Output & Metrics: OAE response magnitude boost (+6 dB compared to click stimuli), flat stimulus ear canal calibration (Β±1 dB), and SNR > 20 dB.
oae_stimulus_equalizer.m 
% Swept-Tone OAE Stimulus Equalization & Cochlear Response
fs = 44100; T = 0.5; t = 0:1/fs:T;
f0 = 500; f1 = 8000;
stimulus = chirp(t, f0, T, f1, 'logarithmic');

% Simulated ear canal acoustic response (resonance at 3 kHz)
[b_ear, a_ear] = butter(2, [2500 3500]/(fs/2), 'bandpass');
ear_response = filter(b_ear, a_ear, stimulus) + 0.2*stimulus;

% Regularized Frequency-Domain Inverse Filter
N_fft = 2048;
H = fft(ear_response, N_fft);
H_inv = conj(H) ./ (abs(H).^2 + 1e-3);
equalized_sig = real(ifft(fft(stimulus, N_fft) .* H_inv));

figure;
subplot(2,1,1); plot(t(1:1000), stimulus(1:1000)); title('Raw Logarithmic Chirp Stimulus');
subplot(2,1,2); plot(t(1:1000), equalized_sig(1:1000), 'r'); title('Pre-Equalized Ear Canal Stimulus');
xlabel('Time (s)');
Est. Duration: 2–4 Weeks Request Custom Project →

10. FIR & IIR Digital Filter Design and Quantization Analysis

Beginner
Toolbox: Signal Processing Toolbox, Fixed-Point Designer Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Design Parks-McClellan equiripple FIR and Elliptic IIR digital filters. Evaluate coefficient quantization degradation across 8-bit, 16-bit, and double-precision implementations.
βš™οΈ Key MATLAB Functions: firpmellipfreqzfvtoolfi
πŸ“Š Expected Output & Metrics: Frequency response overlay plots, limit-cycle oscillation detection, stopband attenuation loss, and pole-zero relocation diagrams.
fir_quantization_effects.m 
% Parks-McClellan Equiripple FIR Filter & 16-Bit Quantization
fs = 48000; fp = 6000; fstop = 9000;
f_pts = [0 fp fstop fs/2]/(fs/2); a_pts = [1 1 0 0];
b_fir = firpm(40, f_pts, a_pts);

% 16-bit Fixed-Point Quantization
b_q = fi(b_fir, 1, 16, 15);
[h_double, f] = freqz(b_fir, 1, 1024, fs);
[h_quant, ~]  = freqz(double(b_q), 1, 1024, fs);

figure;
plot(f/1000, 20*log10(abs(h_double)), 'b', f/1000, 20*log10(abs(h_quant)), 'r--', 'LineWidth', 1.5);
grid on; xlabel('Frequency (kHz)'); ylabel('Magnitude (dB)');
legend('Double Precision FIR', '16-bit Quantized FIR');
title('FIR Filter Quantization Magnitude Response');
Est. Duration: 4–6 Hours Request Custom Project →

11. Adaptive Noise Cancellation Using LMS Algorithm

Intermediate
Toolbox: DSP System Toolbox, Signal Processing Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Remove correlated 50/60 Hz power-line hum and acoustic echoes from desired speech signals using the Least Mean Squares (LMS) adaptive FIR filtering algorithm.
βš™οΈ Key MATLAB Functions: dsp.LMSFilterfiltersnrpwelchmean
πŸ“Š Expected Output & Metrics: Mean Squared Error (MSE) learning curves, adaptive weight trajectory convergence, and output SNR enhancement > 22 dB.
lms_adaptive_cancellation.m 
% LMS Adaptive Noise Cancellation Simulation
fs = 8000; N = 4000; t = (0:N-1)'/fs;
% Desired speech harmonic signal
s = sin(2*pi*300*t) + 0.5*sin(2*pi*700*t);

% Correlated 50 Hz interference
noise_ref = sin(2*pi*50*t);
noise_dist = 0.8*sin(2*pi*50*t + pi/4) + 0.2*sin(2*pi*150*t);
primary_in = s + noise_dist;

% LMS Adaptive Filter
lms = dsp.LMSFilter('Length', 32, 'StepSize', 0.01);
[y, e] = lms(noise_ref, primary_in);

figure;
subplot(2,1,1); plot(t(1:500), primary_in(1:500), 'r'); title('Noisy Primary Signal');
subplot(2,1,2); plot(t(1:500), e(1:500), 'g', 'LineWidth', 1.2); title('LMS Error Output (Recovered Speech)');
xlabel('Time (s)');
Est. Duration: 1–2 Weeks Request Custom Project →

12. Real-Time FFT Audio Spectrum Analyzer & Harmonic Distortion (THD) Measurement

Beginner
Toolbox: Audio Toolbox, Signal Processing Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Implement an N-point windowed FFT audio spectrum visualizer computing Total Harmonic Distortion (THD), Signal-to-Noise-and-Distortion (SINAD), and Spurious-Free Dynamic Range (SFDR).
βš™οΈ Key MATLAB Functions: fftthdsinadsfdrhann
πŸ“Š Expected Output & Metrics: Dynamic dBFS audio spectral display, automated fundamental frequency peak detection, and quantitative THD / SINAD measurements.
audio_fft_thd_analyzer.m 
% Real-Time Audio FFT & THD Measurement
fs = 44100; N = 2048; t = (0:N-1)/fs;
% 1 kHz Test Tone with 2nd & 3rd Harmonics
x = sin(2*pi*1000*t) + 0.04*sin(2*pi*2000*t) + 0.015*sin(2*pi*3000*t);

% Hann-Windowed FFT
w = hann(N)';
X_fft = fft(x .* w, N);
mag_db = 20*log10(abs(X_fft(1:N/2+1)) / (N/4));
f_axis = (0:N/2)*(fs/N);

% Compute Distortion Metrics
thd_val = thd(x, fs);
sinad_val = sinad(x, fs);

figure; plot(f_axis/1000, mag_db, 'LineWidth', 1.5); grid on;
xlabel('Frequency (kHz)'); ylabel('Magnitude (dBFS)');
title(sprintf('Audio FFT Spectrum (THD = %.2f dB, SINAD = %.2f dB)', thd_val, sinad_val));
Est. Duration: 4–6 Hours Request Custom Project →

13. DTMF Tone Generator and Goertzel Algorithm Dual-Tone Decoder

Beginner
Toolbox: Signal Processing Toolbox, DSP System Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Implement ITU-T standard Dual-Tone Multi-Frequency (DTMF) keypad signal generation and decode telephone touch-tones efficiently in real-time using the Goertzel single-frequency DFT filter algorithm.
βš™οΈ Key MATLAB Functions: goertzelsoundscaudiowriteround
πŸ“Š Expected Output & Metrics: 100% DTMF keypad touch-tone detection accuracy in AWGN channels down to 0 dB SNR, computational speedup > 8x vs full FFT.
dtmf_goertzel_decoder.m 
% DTMF Encoder & Goertzel Tone Decoder
fs = 8000; t = 0:1/fs:0.1; % 100 ms burst
row_f = [697 770 852 941]; col_f = [1209 1336 1477 1633];
keypad = ['1' '2' '3' 'A'; '4' '5' '6' 'B'; '7' '8' '9' 'C'; '*' '0' '#' 'D'];

% Transmit Key '9' (852 Hz + 1477 Hz)
tx_sig = sin(2*pi*row_f(3)*t) + sin(2*pi*col_f(3)*t) + 0.3*randn(size(t));

% Goertzel Algorithm at 8 Standard DTMF Frequencies
test_freqs = [row_f col_f];
indices = round(test_freqs/fs * length(tx_sig)) + 1;
dft_vals = goertzel(tx_sig, indices);
energies = abs(dft_vals).^2;

[~, r_idx] = max(energies(1:4));
[~, c_idx] = max(energies(5:8));
fprintf('Decoded Key: %s (Transmission Verified!)\n', keypad(r_idx, c_idx));
Est. Duration: 4–6 Hours Request Custom Project →

14. Multirate Polyphase Decimator and Interpolator Filter Bank

Intermediate
Toolbox: DSP System Toolbox, Signal Processing Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Implement computationally efficient Noble Identity polyphase decomposition filter banks for multirate decimation (downsampling) and interpolation (upsampling) without spectral imaging or aliasing.
βš™οΈ Key MATLAB Functions: dsp.FIRDecimatordsp.FIRInterpolatordesignfiltpwelch
πŸ“Š Expected Output & Metrics: Aliasing suppression > 60 dB, 4x arithmetic operation reductions via polyphase FIR commutators, and PSD validation.
polyphase_decimation_demo.m 
% 4x Polyphase Decimation with Anti-Aliasing FIR
M = 4; fs_in = 96000; fs_out = fs_in / M;
t_in = 0:1/fs_in:0.02;
% Signal with 4 kHz Passband & 30 kHz Aliasing Component
x = sin(2*pi*4000*t_in) + sin(2*pi*30000*t_in);

% Design Anti-Aliasing Lowpass FIR
d_fir = designfilt('lowpassfir', 'PassbandFrequency', 9000, ...
    'StopbandFrequency', 12000, 'SampleRate', fs_in);
decimator = dsp.FIRDecimator('DecimationFactor', M, 'Numerator', d_fir.Coefficients);
y = decimator(x');

figure;
subplot(2,1,1); pwelch(x, 256, 128, 512, fs_in); title('Input Spectrum @ 96 kHz');
subplot(2,1,2); pwelch(y, 64, 32, 128, fs_out); title('Decimated Output Spectrum @ 24 kHz (Aliasing Suppressed)');
Est. Duration: 1–2 Weeks Request Custom Project →

15. Discrete Wavelet Transform (DWT) 2D Image Denoising and Compression

Intermediate
Toolbox: Wavelet Toolbox, Image Processing Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Implement multi-level 2D Discrete Wavelet Transform (DWT) decomposition using Symlet and Daubechies wavelets. Apply Donoho VisuShrink soft thresholding for simultaneous noise reduction and high-ratio data compression.
βš™οΈ Key MATLAB Functions: wavedec2waverec2wthreshpsnrdwt2
πŸ“Š Expected Output & Metrics: Peak Signal-to-Noise Ratio (PSNR > 30 dB), Structural Similarity Index (SSIM > 0.92), and 4:1 image compression ratio.
dwt_2d_denoising_compression.m 
% 2D DWT Decomposition & VisuShrink Denoising
I = im2double(imread('cameraman.tif'));
I_noisy = I + 0.08*randn(size(I));

% Level-2 Wavelet Decomposition with Symlet-4
[c, s] = wavedec2(I_noisy, 2, 'sym4');

% Donoho Universal Thresholding
sigma = median(abs(c)) / 0.6745;
thr = sigma * sqrt(2 * log(numel(I)));
c_clean = wthresh(c, 's', thr);

% Reconstruction
I_clean = waverec2(c_clean, s, 'sym4');
fprintf('PSNR Noisy: %.2f dB | PSNR Wavelet Cleaned: %.2f dB\n', ...
    psnr(I_noisy, I), psnr(I_clean, I));

figure; imshowpair(I_noisy, I_clean, 'montage');
title('Noisy Input Image (Left) vs Wavelet Cleaned Output (Right)');
Est. Duration: 1–2 Weeks Request Custom Project →

16. Cochlear Implant Speech Processing Simulation using CIS Strategy

Advanced
Toolbox: Audio Toolbox, Signal Processing Toolbox Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Simulate an 8-to-16 channel Continuous Interleaved Sampling (CIS) bio-signal processing strategy for auditory neuro-prosthetics, extracting instantaneous speech envelopes via Hilbert transform.
βš™οΈ Key MATLAB Functions: hilbertbutterfilterdesignfiltsoundsc
πŸ“Š Expected Output & Metrics: 8-channel tonotopic electrode pulse electrodograms, synthesized acoustic vocoder intelligibility evaluation, and spectral envelope plots.
cochlear_implant_cis_sim.m 
% 8-Channel Cochlear Implant CIS Strategy Simulation
fs = 16000; t = 0:1/fs:1;
speech_in = sin(2*pi*250*t).*(1 + 0.5*sin(2*pi*4*t)) + 0.3*sin(2*pi*1500*t);

% Logarithmically Spaced Frequency Channels (300 Hz - 5500 Hz)
bands = round(logspace(log10(300), log10(5500), 9));
vocoded_out = zeros(size(speech_in));

for ch = 1:8
    % 1. Channel Bandpass Filter
    [b, a] = butter(2, [bands(ch) bands(ch+1)]/(fs/2), 'bandpass');
    b_sig = filter(b, a, speech_in);
    
    % 2. Hilbert Envelope Extraction
    env = abs(hilbert(b_sig));
    [b_lp, a_lp] = butter(2, 200/(fs/2), 'low');
    env_smooth = filter(b_lp, a_lp, env);
    
    % 3. Modulate Tone Carrier at Center Frequency
    fc = sqrt(bands(ch) * bands(ch+1));
    vocoded_out = vocoded_out + env_smooth .* sin(2*pi*fc*t);
end

figure; plot(t(1:800), speech_in(1:800), 'b', t(1:800), vocoded_out(1:800), 'r', 'LineWidth', 1.2);
legend('Original Speech', '8-Channel Vocoded Acoustic Output');
title('Continuous Interleaved Sampling (CIS) Cochlear Vocoder Simulation');
Est. Duration: 3–5 Weeks Request Custom Project →

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Frequently Asked Questions

Everything you need to know about implementing Digital Signal Processing (DSP) MATLAB projects.

While general signal processing covers both continuous-time and analog signals, Digital Signal Processing (DSP) specifically focuses on discrete-time numerical data sequences, digital filter structures (FIR/IIR), z-plane stability, fixed-point quantization effects, accumulator bit growth, and hardware deployment on DSP processors (e.g. TI TMS320) or FPGAs.

Use FIR filters (designed with fir1, firpm, or designfilt) when you require exact linear phase (no waveform dispersion), guaranteed BIBO stability, and symmetric impulse responseβ€”ideal for audio equalization, biomedical ECG, and image processing. Use IIR filters (Butterworth, Chebyshev, Elliptic) when low computational latency and sharp transition cutoffs with minimal filter order are required on memory-constrained microcontrollers.

Use the Fixed-Point Designer toolbox with MATLAB fi objects. You can specify exact signedness, word lengths (e.g. 16-bit or 24-bit), fraction lengths, overflow rules (Saturate vs Wrap), and rounding modes (Floor vs Nearest). You can simulate MAC accumulator guard bits and analyze quantization noise using freqz and qerror.

Yes. MATLAB provides automated hardware code generation:
  • MATLAB Coder: Generates standalone ANSI/ISO C/C++ from MATLAB scripts.
  • Embedded Coder: Generates target-optimized C code for ARM Cortex-M/R/A and Texas Instruments DSPs.
  • HDL Coder: Synthesizes synthesizable VHDL and Verilog code for Xilinx and Intel FPGA hardware.

High-impact capstone projects include:
  • Project 4: Synthetic Aperture Radar (SAR) 2D Reconstruction.
  • Project 5: Multiple Input Digital Stethoscope Signal Processing.
  • Project 11: Real-Time LMS Adaptive Noise Cancellation.
  • Project 16: Cochlear Implant Continuous Interleaved Sampling (CIS) Vocoder.

All projects run entirely in software simulation on standard MATLAB Student, Home, or Campus licenses equipped with the Signal Processing and DSP System toolboxes. No external hardware is mandatory to generate plots, metrics, and audio playback.

Our senior PhD DSP engineering team provides 24/7 custom code development, debugging, and one-on-one consultation. Submit your requirements here or chat directly with our specialists on WhatsApp.

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