Rust: library for frequency spectrum analysis using FFT

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.

I'm not an expert on digital signal processing. Code contributions are highly welcome! :)

How to use

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 implement
    // 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,
        // sample 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)
    }
}

How to scale values

// e.g. like this
fn get_scale_to_one_fn_factory() -> SpectrumTotalScaleFunctionFactory{
    Box::new(
        move |min: f32, max: f32, average: f32, median: f32| {
            Box::new(
                move |x| x/max
            )
        }
    )
}
fn main() {
    // ...
    let spectrum_hann_window = samples_fft_to_spectrum(
        &hann_window,
        44100,
        FrequencyLimit::All,
        None,
        // optional total scaling at the end; see doc comments
        Some(get_scale_to_one_fn_factory()),
    );
}

Performance

Measurements taken on i7-8650U @ 3 Ghz (Single-Core) with optimized build

Example visualization

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 sample 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!

Spectrum without window function on samples

Peaks (50, 1000, 3777 Hz) are clearly visible but also noise.

Hann window function on samples before FFT

Peaks (50, 1000, 3777 Hz) are clearly visible and overall noise is much smaller, but a few noisy peaks.

Hamming window function on samples before FFT

Peaks (50, 1000, 3777 Hz) are clearly visible and overall noise is smaller.

Trivia / FAQ

Why f64 and no f32?

I tested f64 but the additional accuracy doesn't pay out the ~40% calculation overhead (on x86_64).

What can I do against the noise?

Apply a window function, like Hann window or Hamming window. But I'm not an expert on this.