Signal Denoising using Convolution in Python

Published: 27 December 2025
on channel: Numeryst

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• Introduction to the Digital Signal Process...
Learn signal denoising using convolution in Python to remove noise and recover clean signals.
In this video, we demonstrate the first application of convolution: denoising a noisy sinusoidal signal. Using a moving average filter, we perform convolution to smooth out the noise while preserving the original signal pattern. This tutorial uses Python and NumPy to generate the signals, add Gaussian noise, and filter the noisy signal step-by-step.

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Timestamps:
00:00 – Introduction: What is Denoising?
00:25 – The Role of Convolution and Kernels in Signal Cleaning
00:49 – Python Setup: Importing Modules
00:54 – Step 1: Generating a Noisy Test Signal
01:03 – Defining Sampling Rate and Time Vectors in NumPy
01:55 – Analyzing the Generated Time Vector Samples
02:25 – Creating the Clean Noise-Free Sinusoidal Signal
02:52 – Generating and Adding Gaussian Noise
03:25 – Visualization: Plotting the Noisy Signal
04:12 – Step 2: Selecting and Designing a Suitable Filter
04:30 – Creating a Moving Average Filter with NumPy
04:56 – Understanding the Theory: How Moving Average Filters Work

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