FFT Resolution Rechner
Berechnen Sie FFT frequency resolution, number of bins, and time window aus sampling rate und FFT size.
Typically a power of 2
Frequency Resolution
46.875 Hz
Frequency Resolution vs Sampling Rate
Formel
## FFT Frequency Resolution The FFT converts time-domain data into frequency-domain bins. The resolution depends on how long you observe the signal. ### Formulas **Frequency Resolution = Sample Rate / FFT Size** **Time Window = FFT Size / Sample Rate** **Useful Bins = FFT Size / 2 + 1** (for real signals) ### Trade-off Better frequency resolution requires longer observation windows. This is the time-frequency uncertainty principle. - Larger FFT = finer frequency resolution but slower updates - Smaller FFT = coarser resolution but faster updates
Lösungsbeispiel
1024-point FFT at 48 kHz sampling rate.
- 01Resolution: 48,000 / 1,024 = 46.875 Hz
- 02Useful bins: 1024/2 + 1 = 513
- 03Max frequency: 48,000 / 2 = 24,000 Hz
- 04Time window: 1024/48,000 x 1000 = 21.33 ms
Häufig Gestellte Fragen
Why must FFT size be a power of 2?
The Cooley-Tukey FFT algorithm requires powers of 2 for efficiency (O(N log N) vs O(N^2)). Some implementations support other sizes.
How do I improve frequency resolution?
Increase the FFT size (longer time window) or use zero-padding. Note: zero-padding interpolates but does not add actual resolution.
What is spectral leakage?
Signals that do not fit exactly into the FFT window spread energy across adjacent bins. Window functions (Hann, Hamming) reduce leakage.
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