Analyze sampled signal overlap with clear numerical output. Compare sequences, inspect buildup, and export polished results for deeper learning today.
| Signal | Start Index | Samples | Sampling Interval | Meaning |
|---|---|---|---|---|
| x[n] | 0 | 1, 2, 3 | 1 | Input sequence |
| h[n] | 0 | 1, 0.5, 0.25 | 1 | Impulse response |
| y[n] | 0 | 1, 2.5, 4.25, 2, 0.75 | 1 | Convolution result |
Continuous form:
\[ y(t)=\int_{-\infty}^{\infty} x(\tau)\,h(t-\tau)\,d\tau \]
Sampled numerical form used here:
\[ y[n]\approx \sum_{k=0}^{N-1} x[k]\cdot h[n-k]\cdot \Delta t \]
The tool evaluates overlapping products of the sampled signals, sums them for each output position, and scales the sum by the chosen sampling interval.
It computes the numerical convolution of two sampled signals. The output shows how one sequence shapes the other across all valid overlaps.
The continuous convolution integral becomes a weighted sum after sampling. The interval Δt scales each product term and improves the approximation.
Yes. The parser accepts integers, decimals, and negative values. Separate numbers with commas or spaces.
They align sequences on the correct sample positions. This matters when signals begin before or after zero and affects the output index range.
Normalization divides each sequence by the sum of its absolute values. It helps compare signal shapes when raw amplitudes differ greatly.
Convolution length equals input length plus response length minus one. Each shift creates a new overlap, extending the result.
The chart plots both input sequences and the convolution result. It helps you inspect overlap behavior, output growth, and decay patterns visually.
It is a sampled numerical approximation. Accuracy depends on your sampling interval and how well the samples represent the original continuous signals.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.