Calculadora de Resolución de Bin FFT
Calcula la resolución espectral, número de bins y ancho de bin de una FFT.
Fórmula
Δf = f_s / N
Cómo Funciona
Fast Fourier Transform (FFT) bin resolution is a fundamental concept in digital signal processing that determines the frequency resolution and precision of spectral analysis. When performing an FFT, the input time-domain signal is divided into discrete frequency bins, with the width of these bins directly related to the sampling rate and number of samples. The bin resolution represents the smallest frequency interval that can be distinguished in the frequency spectrum, which is calculated as the sampling frequency divided by the total number of samples.
Ejemplo Resuelto
Given a sampling frequency of 10,000 Hz and 1024 total samples, the FFT bin resolution would be calculated as follows: 1. Bin Resolution = Sampling Frequency / Number of Samples 2. Bin Resolution = 10,000 Hz / 1024 3. Bin Resolution ≈ 9.76 Hz This means each frequency bin represents approximately 9.76 Hz of the spectrum, and the maximum resolvable frequency (Nyquist frequency) would be half the sampling rate.
Consejos Prácticos
- ✓Increase the number of samples to improve frequency resolution
- ✓Use zero-padding techniques to interpolate between existing frequency bins
- ✓Consider the trade-off between time domain and frequency domain resolution
- ✓Always ensure sampling rate is at least twice the highest frequency of interest
Errores Comunes
- ✗Confusing bin resolution with sampling frequency
- ✗Not understanding the relationship between sample count and frequency precision
- ✗Assuming uniform resolution across the entire spectrum
Preguntas Frecuentes
How does sample count affect FFT bin resolution?
Increasing the number of samples decreases the bin width, providing higher frequency resolution. More samples allow finer granularity in frequency analysis.
What is the Nyquist frequency?
The Nyquist frequency is half the sampling rate, representing the maximum frequency that can be accurately represented in a digital signal.
Can I improve resolution without increasing sample count?
Zero-padding can help interpolate between existing bins, but it doesn't create new spectral information. True resolution improvement requires more samples.
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