A simple and fast no_std
library to get the frequency spectrum of a digital signal (e.g. audio) using FFT.
It follows the KISS principle and consists of simple building blocks/optional features. In short, this is
a convenient wrapper around several FFT implementations which you can choose from during compilation time
via Cargo features.
I'm not an expert on digital signal processing. Code contributions are highly welcome! 🙂
The MSRV (minimum supported Rust version) is 1.51 Stable because this crate needs the
"resolver" feature of Cargo to cope with build problems occurring in microfft
-crate.
Please see file /EDUCATIONAL.md.
Most tips and comments are located inside the code, so please check out the repository on Github! Anyway, the most basic usage looks like this:
This crate offers multiple FFT implementations using the crates rustfft
and microfft
- both are great, shout-out to
the original creators and all contributors! spectrum-analyzer
offers three features, where exactly one feature
is allowed to be activated, otherwise the build breaks! To see differences between the implementations, plot the results
or look into the screenshots of this README.
rustfft-complex
default, std (recommended): for regular applications, most accurate and most performancemicrofft-complex
no_std (recommended): more accurate thanmicrofft-real
microfft-real
no_std, less accurate but faster thanmicrofft-complex
# ONLY NEEDED FOR `no_std`-builds!
# fixes `no_std` build problems caused by wrong feature resolution of Cargo
# This works since Rust 1.51 (stable)
resolver = "2"
# by default feature "rustfft-complex" is used
spectrum-analyzer = "<latest>"
# or for no_std/microcontrollers
spectrum-analyzer = { version = "<latest>", default-features = false, features = "microfft-complex" }
# or
spectrum-analyzer = { version = "<latest>", default-features = false, features = "microfft-real" }
use spectrum_analyzer::{samples_fft_to_spectrum, FrequencyLimit};
use spectrum_analyzer::windows::hann_window;
fn main() {
// This lib also works in `no_std` environments!
let samples: &[f32] = get_samples(); // TODO you need to implement the samples source
// apply hann window for smoothing; length must be a power of 2 for the FFT
let hann_window = hann_window(&samples[0..4096]);
// calc spectrum
let spectrum_hann_window = samples_fft_to_spectrum(
// (windowed) samples
&hann_window,
// sampling rate
44100,
// optional frequency limit: e.g. only interested in frequencies 50 <= f <= 150?
FrequencyLimit::All,
// optional per element scaling function, e.g. `20 * log10(x)`; see doc comments
None,
// optional total scaling at the end; see doc comments
None,
);
for (fr, fr_val) in spectrum_hamming_window.raw_data().iter() {
println!("{}Hz => {}", fr, fr_val)
}
}
As already mentioned, there are lots of comments in the code. Short story is:
Type ComplexSpectrumScalingFunction
can do anything like BasicSpectrumScalingFunction
whereas BasicSpectrumScalingFunction
is easier to write, especially for Rust beginners.
Measurements taken on i7-8650U @ 3 Ghz (Single-Core) with optimized build and using rustfft
as FFT implementation
Operation | Time |
---|---|
Hann Window with 4096 samples | ≈70µs |
Hamming Window with 4096 samples | ≈10µs |
Hann Window with 16384 samples | ≈175µs |
Hamming Window with 16384 samples | ≈44µs |
FFT to spectrum with 4096 samples @ 44100Hz | ≈240µs |
FFT to spectrum with 16384 samples @ 44100Hz | ≈740µs |
In the following example you can see a basic visualization of frequencies 0 to 4000Hz
for
a layered signal of sine waves of 50
, 1000
, and 3777Hz
@ 41000Hz
sampling rate. The peaks for the
given frequencies are clearly visible. Each calculation was done with 2048
samples, i.e. ≈46ms.
The noise (wrong peaks) also comes from clipping of the added sine waves!
Peaks (50, 1000, 3777 Hz) are clearly visible but also some noise.
Peaks (50, 1000, 3777 Hz) are clearly visible and Hann window reduces noise a little bit. Because this example has few noise, you don't see much difference.
Peaks (50, 1000, 3777 Hz) are clearly visible and Hamming window reduces noise a little bit. Because this example has few noise, you don't see much difference.
I tested f64 but the additional accuracy doesn't pay out the ~40% calculation overhead (on x86_64).
Apply a window function, like Hann window or Hamming window. But I'm not an expert on this.
- Interpreting FFT Results: https://www.gaussianwaves.com/2015/11/interpreting-fft-results-complex-dft-frequency-bins-and-fftshift/
- FFT basic concepts: https://www.youtube.com/watch?v=z7X6jgFnB6Y
- „The Fundamentals of FFT-Based Signal Analysis and Measurement“ https://www.sjsu.edu/people/burford.furman/docs/me120/FFT_tutorial_NI.pdf
- Fast Fourier Transforms (FFTs) and Windowing: https://www.youtube.com/watch?v=dCeHOf4cJE0
Also check out my blog post! https://phip1611.de/2021/03/programmierung-und-skripte/frequency-spectrum-analysis-with-fft-in-rust/
The FFT implementations have different advantages and your decision for one of them is a tradeoff between accuracy and computation time. The following two screenshots (60 and 100 Hz sine waves) visualize a spectrum obtained by real FFT respectively complex FFT. The complex FFT result is much smoother and more accurate as you can clearly see.
⚠ Because of a frequency resolution of ~10Hz in this example (4096 samples, 44100Hz sampling rate), the peaks are not exactly at 60/100 Hz. ⚠