Visualize continuous and discrete convolution step by step. Slide the flipped kernel across the signal and see the output build.
Convolution Visualizer exposes the overlap behind continuous and discrete convolution. It flips the kernel, moves it across the signal, and shows how the products at each position contribute to the output. This makes the tool useful for connecting the convolution formula to filtering and impulse-response behavior. The common source of confusion is the flip: sliding an unflipped kernel describes cross-correlation, not convolution.
A concrete input and expected output from the current implementation.
Input
Discrete signal x[n] = [1, 2]; kernel h[n] = [1, 1]; both begin at n = 0
Expected output
Full convolution y[n] = [1, 3, 2] for n = 0, 1, 2
The output samples are 1*1 = 1, then 1*1 + 2*1 = 3, then 2*1 = 2. The output has three samples because the two length-2 sequences overlap at three distinct shifts.
For discrete sequences, the value at output index n is the sum of x[k]h[n-k] over the samples that overlap. At each step, the visualizer associates the current alignment with its pointwise products and their sum. Moving through every alignment builds the full finite convolution from its first partial overlap to its last.
For continuous signals, the corresponding value at shift t is the integral of x(tau)h(t-tau) over tau. The flipped and shifted second function is multiplied by the first wherever their supports overlap. The area under that product is one point of the convolution output, so changing the shift traces the resulting function.
Kernel reversal is part of convolution itself. In the discrete formula h[n-k], increasing k reads the kernel in the opposite direction before the shift is applied. If the kernel is not reversed, the sliding product computes cross-correlation instead. The two results can still match when the kernel is symmetric.
No. A normalized averaging kernel can smooth rapid changes, but convolution is not inherently a smoothing operation. Difference kernels can emphasize changes, an impulse can preserve the signal, and other kernels can introduce shifts or oscillatory responses. The displayed result depends on the kernel values, their indexing, and how they overlap the signal.
The example and behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.
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