How do you implement DSP algorithms in MATLAB or Python?

Implementing DSP (Digital Signal Processing) algorithms in MATLAB or Python involves using built-in libraries and signal processing toolkits to analyze, filter, transform, and synthesize signals.

Here's how to do it in both environments:

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Common DSP Algorithms

• FIR/IIR filtering

• FFT and spectral analysis

• Convolution and correlation

• Signal generation and noise removal

• Modulation/demodulation

• Feature extraction (e.g., MFCC, envelope, RMS)

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1. MATLAB Implementation

MATLAB is widely used in DSP due to its built-in functions and toolboxes.

Example: Low-pass FIR Filter

matlab

Fs = 1000;                 % Sampling frequency
t = 0:1/Fs:1;              % Time vector
x = sin(2*pi*50*t) + sin(2*pi*200*t); % Composite signal

% Design FIR low-pass filter
fc = 100;                  % Cutoff frequency
n = 50;                    % Filter order
b = fir1(n, fc/(Fs/2));    % Normalize frequency

% Apply filter
y = filter(b, 1, x);

% Plot
plot(t, x, 'b', t, y, 'r');
legend('Original', 'Filtered');

🛠 Useful MATLAB DSP Functions

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2. Python Implementation

Python uses libraries like NumPy, SciPy, Matplotlib, and scikit-dsp-comm or PyDSP.

Example: FIR Filter with SciPy

python

import numpy as np
from scipy.signal import firwin, lfilter
import matplotlib.pyplot as plt

Fs = 1000
t = np.linspace(0, 1, Fs, endpoint=False)
x = np.sin(2 * np.pi * 50 * t) + np.sin(2 * np.pi * 200 * t)

# Design FIR filter
fc = 100
numtaps = 51
b = firwin(numtaps, fc, fs=Fs)

# Apply filter
y = lfilter(b, 1.0, x)

# Plot
plt.plot(t, x, label='Original')
plt.plot(t, y, label='Filtered', color='red')
plt.legend()
plt.show()

Useful Python DSP Libraries

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Choosing MATLAB vs Python

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Optional: Real-time DSP in Python

python

import sounddevice as sd

def callback(indata, outdata, frames, time, status):
    # Simple pass-through or filter processing
    outdata[:] = indata

with sd.Stream(callback=callback):
    sd.sleep(10000)  # Run for 10 seconds