Inspect DFT bin offset and window effects through scalloping loss, leakage floor, and equivalent noise bandwidth.
Parameters
Sample count N
count
Input Sample count N.
Bin offset
bin
Input Bin offset.
Window sidelobe
dB
Input Window sidelobe.
Coherent gain
-
Input Coherent gain.
While paused, move the sliders to update the result instantly.
Spectral leakage and windowing (live)
When a sinusoid is sampled over a finite window and transformed with the DFT, a frequency that is not an integer number of cycles in the window "leaks" and smears across many bins. The signal frequency sweeps across bin boundaries, and switching the window (Rectangular / Hann / Hamming / Blackman) lowers side-lobe leakage at the cost of a wider main lobe. The green bar is the intended bin; the orange bars are leakage.
This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks.
How to read it
Use the main plot to read the controlling trend, including break points that a single result card can hide.
Use the sensitivity view to find input combinations where margin collapses quickly.
For early design, focus on which input controls margin before trusting the absolute value.
Learn DFT Leakage Window Detail by dialogue
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When reading DFT Leakage Window Detail, where should I look first? Moving Sample count N changes both the plots and the result cards.
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Start with Scalloping loss, but do not treat the number as the whole answer. Use Windowed spectrum to confirm the assumed state, then read Scalloping and leakage for the distribution or trend. Use the main plot to read the controlling trend, including break points that a single result card can hide.
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I can see why Sample count N changes Scalloping loss. How should I judge the influence of Bin offset?
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Move Bin offset in small steps and watch Equivalent noise bandwidth. That reveals which term is controlling the result. This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks. A single operating point is not enough; sweep the realistic scatter range.
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What is Offset-window attenuation map for? It feels like the ordinary curve already tells the story.
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Offset-window attenuation map is for finding boundaries where the condition becomes risky or margin collapses quickly. Use the sensitivity view to find input combinations where margin collapses quickly. In First-pass comparison of design options before review, the important question is often what happens after a small change, not only the nominal value.
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So if Scalloping loss is within the target, can I accept the condition?
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Treat this as a first-pass review. It helps with Narrowing controlling factors and worst-side conditions before detailed analysis and Teaching or explaining the equation, numbers, and visualization under the same inputs, but final decisions still need standards, measured data, detailed analysis, and vendor limits. For early design, focus on which input controls margin before trusting the absolute value.
Practical use
First-pass comparison of design options before review.
Narrowing controlling factors and worst-side conditions before detailed analysis.
Teaching or explaining the equation, numbers, and visualization under the same inputs.
FAQ
Start with Scalloping loss and Equivalent noise bandwidth. Then use Windowed spectrum to confirm the assumed state and Scalloping and leakage to read distribution or bias. Use the main plot to read the controlling trend, including break points that a single result card can hide
Move Sample count N alone, then move Bin offset by a comparable amount and compare the change in Scalloping loss. Offset-window attenuation map shows combinations where margin or performance changes quickly.
Use it for First-pass comparison of design options before review. Instead of trusting a single point, widen the input range and check whether Scalloping loss keeps enough margin before moving to detailed analysis.
This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks. Final decisions still require standards, measured data, detailed analysis, and vendor limits.
How to Use
Enter FFT length (N) — typical values 256, 512, 1024, 4096 samples for DFT analysis
Set frequency bin offset (0 to 1.0) representing signal offset from nearest bin center as fraction of bin width
Select window function via sidelobe parameter — Rectangular (-13dB), Hann (-32dB), Hamming (-43dB), or Blackman (-58dB)
Observe scalloping loss (dB), equivalent noise bandwidth (ENBW in bins), leakage floor (dB relative to peak), and frequency resolution (Hz)
Adjust parameters iteratively to minimize spectral leakage for your measurement bandwidth and dynamic range requirements
Worked Example
Using this tool's defaults (N=1024, bin offset 0.22, window sidelobe −42 dB, coherent gain 0.50): scalloping loss ≈ 0.70 dB, ENBW = 1/coherent-gain = 2.00 bins, leakage floor ≈ −53.4 dB, and frequency resolution = 1/N ≈ 0.000977. At the worst-case offset of 0.5 the scalloping loss rises to ≈ 3.92 dB (the rectangular-window sinc response the tool models). Lowering the coherent gain raises the ENBW — the classic resolution-versus-leakage trade-off.
Practical Notes
Scalloping loss peaks at 0.5 bin offset; use zero-padding or higher N to reduce bin width and push worst-case loss below measurement uncertainty
Rectangular window shows zero scalloping loss only when signal aligns exactly with bin center — impractical for unknown frequencies; avoid in production measurements
ENBW directly affects noise floor; a signal at -80 dBFS masked by thermal noise requires ENBW < 1.2 bins to remain detectable in 16-bit ADC systems
For chirp or swept-sine testing, bin offset changes continuously; monitor leakage floor variation across sweep range using window with steepest sidelobe rolloff