FFT resolução frequência
- Created by
- Renato Passos, Eng. de Software
- Reviewed by
- Renato Passos, Eng. de Software
Last updated: Apr 18, 2026
About this calculator
The FFT frequency resolution calculator determines the smallest detectable frequency difference in a signal analysis. This resolution depends on the sampling rate (Fs) and the number of samples (N), using the formula Fs/N. Higher sample counts yield finer resolution, enabling identification of closely spaced frequency components.
Frequency resolution is critical in applications like audio analysis, signal processing, and vibration diagnostics. For example, in audio systems, low resolution might miss subtle frequencies, while high resolution demands more processing. The Fs/N formula helps determine the theoretical precision of the frequency spectrum.
When using the calculator, consider that resolution is limited by the sampled signal duration. If the sampling rate is too low, aliasing distortion may occur, compromising analysis. Additionally, short samples reduce resolution, requiring a balance between computational speed and accuracy.
For practical applications, use the largest N possible within the system's computational capacity. Signals with very close frequencies, such as in ECGs, benefit from high resolution. In industrial equipment monitoring, a lower resolution may suffice for detecting common faults.
Frequently asked questions
What is frequency resolution in FFT?
It's the smallest difference in frequency that the algorithm can distinguish between two spectral components, calculated as the sampling rate (Fs) divided by the number of samples (N).
Why is frequency resolution important in signal analysis?
It determines the precision with which distinct frequencies are identified, crucial for applications like equipment fault detection or audio analysis.
How does sampling rate affect resolution?
Higher rates improve resolution but require more processing. However, the main resolution depends on the Fs/N ratio, not the rate alone.
What happens with low frequency resolution?
Closely spaced frequency components may overlap, hiding signal details. This is critical for applications like vibration analysis in machines.
How to choose the ideal number of samples (N)?
Increase N to improve resolution, but consider sampling duration and computational capacity. For short signals, use the maximum feasible N.