Convolution Calculator

Compute Convolution of Sequences

Calculate the convolution of two sequences or functions - a mathematical operation fundamental to signal processing, probability theory, and differential equations. Our calculator performs discrete and continuous convolutions.

Compute Discrete Convolution of Two Sequences

Enter two numerical sequences to calculate their discrete convolution. The result will be displayed numerically and graphically.

Understanding Convolution

Convolution is a mathematical operation used to determine the integral that expresses the amount of overlap of one function as it is shifted over another function. In simpler terms, convolution blends two functions to produce a third function that expresses how the shape of one is modified by the other. This operation is fundamental in various fields such as signal processing, image processing, and probability theory.

What is Convolution?

Convolution combines two sequences (or functions) to form a third sequence that represents how the shape of one is modified by the other. In discrete convolution, commonly used in digital signal processing, two finite sequences are combined to produce a third sequence of length equal to the sum of the lengths of the two original sequences minus one.

Applications of Convolution

  • Signal Processing: Convolution is used to apply filters to signals, such as blurring or sharpening in image processing or noise reduction in audio signals.
  • Image Processing: Techniques like edge detection and image enhancement rely on convolution with specific kernels.
  • Probability Theory: Convolution is used to find the distribution of the sum of independent random variables.
  • Machine Learning: Convolutional Neural Networks (CNNs) use convolution operations to detect features in data.

How Does Convolution Work?

To perform discrete convolution, follow these steps:

  1. Reverse: Reverse one of the sequences. This is often done implicitly in computations.
  2. Shift: Shift the reversed sequence across the other sequence.
  3. Multiply and Sum: At each shift position, multiply overlapping elements and sum the results to produce a single value in the convolution result.

Example of Discrete Convolution

Consider two sequences:

  • Sequence A: 1, 2, 3
  • Sequence B: 4, 5, 6

The convolution of A and B is calculated as follows:

nA * B
01×4 = 4
11×5 + 2×4 = 5 + 8 = 13
21×6 + 2×5 + 3×4 = 6 + 10 + 12 = 28
32×6 + 3×5 = 12 + 15 = 27
43×6 = 18

So, the convolution result is: 4, 13, 28, 27, 18

Using the Convolution Calculator

To use the Convolution Calculator:

  1. Enter Sequences: Input two numerical sequences separated by commas in the provided fields. For example, 1, 2, 3 and 4, 5, 6.
  2. Calculate: Click the Calculate button to compute the convolution.
  3. View Results: The convolution result will be displayed in a table and visualized on a graph.
  4. Reset: Use the Reset button to clear inputs and results.

Understanding the Graph

The graph provides a visual representation of the two input sequences and their convolution. Each sequence is plotted in a different color for clarity:

  • Sequence A: Blue line
  • Sequence B: Red line
  • Convolution A * B: Green line

This visualization helps in understanding how the convolution combines the two sequences to produce the resulting sequence.

What is Convolution?

Convolution combines two functions to produce a third. For discrete sequences: (f*g)[n] = Σ f[k]g[n-k]. For continuous: (f*g)(t) = ∫ f(τ)g(t-τ)dτ. Intuitively, it measures the overlap between one function and a reversed, shifted version of another. Commutative: f*g = g*f.

Applications in Signal Processing

Convolution is central to: filtering signals (impulse response * input), image processing (blurring, edge detection), audio effects (reverb, echo), system analysis (input-output relationship), and communications (pulse shaping). The convolution theorem links time and frequency domains via Fourier transform.

Related Concepts

Cross-correlation: similar to convolution but without reversing. Deconvolution: inverse operation (used in image restoration). Circular convolution: periodic signals. FFT enables fast convolution via multiplication in frequency domain. Understanding convolution is essential for modern signal processing and machine learning.