1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402
/*
MIT License
Copyright (c) 2021 Philipp Schuster
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
//! 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 a FFT implementation. You choose the
//! implementation at compile time via Cargo features. This crate uses
//! "microfft" by default for FFT. See README for more advise.
//!
//! ## Examples
//! ### Scaling via dynamic closure
//! ```rust
//! use spectrum_analyzer::{samples_fft_to_spectrum, FrequencyLimit};
//! // get data from audio source
//! let samples = vec![0.0, 1.1, 5.5, -5.5];
//! let res = samples_fft_to_spectrum(
//! &samples,
//! 44100,
//! FrequencyLimit::All,
//! Some(&|val, info| val - info.min),
//! );
//! ```
//! ### Scaling via static function
//! ```rust
//! use spectrum_analyzer::{samples_fft_to_spectrum, FrequencyLimit};
//! use spectrum_analyzer::scaling::scale_to_zero_to_one;
//! // get data from audio source
//! let samples = vec![0.0, 1.1, 5.5, -5.5];
//! let res = samples_fft_to_spectrum(
//! &samples,
//! 44100,
//! FrequencyLimit::All,
//! Some(&scale_to_zero_to_one),
//! );
//! ```
#![deny(
clippy::all,
clippy::cargo,
clippy::nursery,
// clippy::restriction,
// clippy::pedantic
)]
// now allow a few rules which are denied by the above statement
// --> they are ridiculous and not necessary
#![allow(
clippy::suboptimal_flops,
clippy::redundant_pub_crate,
clippy::fallible_impl_from
)]
#![deny(missing_debug_implementations)]
#![deny(rustdoc::all)]
#![no_std]
#[cfg(any(
all(feature = "microfft-real", feature = "microfft-complex"),
all(feature = "microfft-real", feature = "rustfft-complex"),
all(feature = "microfft-complex", feature = "rustfft-complex"),
not(any(
feature = "microfft-real",
feature = "microfft-complex",
feature = "rustfft-complex"
))
))]
compile_error!(
"You must use exactly one FFT implementation. Check Cargo compile-time features of this crate!"
);
// enable std in tests (println!() for example)
#[cfg_attr(test, macro_use)]
#[cfg(test)]
extern crate std;
// We use alloc crate, because this is no_std
// The macros are only needed when we test
#[cfg_attr(test, macro_use)]
extern crate alloc;
use alloc::vec::Vec;
use crate::error::SpectrumAnalyzerError;
use crate::fft::{Complex32, Fft, FftImpl};
pub use crate::frequency::{Frequency, FrequencyValue};
pub use crate::limit::FrequencyLimit;
pub use crate::limit::FrequencyLimitError;
use crate::scaling::SpectrumScalingFunction;
pub use crate::spectrum::FrequencySpectrum;
pub mod error;
mod fft;
mod frequency;
mod limit;
pub mod scaling;
mod spectrum;
mod util;
pub mod windows;
// test module for large "integration"-like tests
#[cfg(test)]
mod tests;
/// Takes an array of samples (length must be a power of 2),
/// e.g. 2048, applies an FFT (using the specified FFT implementation) on it
/// and returns all frequencies with their volume/magnitude.
///
/// By default, no normalization/scaling is done at all and the results,
/// i.e. the frequency magnitudes/amplitudes/values are the raw result from
/// the FFT algorithm, except that complex numbers are transformed
/// to their magnitude.
///
/// * `samples` raw audio, e.g. 16bit audio data but as f32.
/// You should apply an window function (like Hann) on the data first.
/// The final frequency resolution is `sample_rate / (N / 2)`
/// e.g. `44100/(16384/2) == 5.383Hz`, i.e. more samples =>
/// better accuracy/frequency resolution.
/// * `sampling_rate` sampling_rate, e.g. `44100 [Hz]`
/// * `frequency_limit` Frequency limit. See [`FrequencyLimit´]
/// * `scaling_fn` See [`crate::scaling::SpectrumScalingFunction`] for details.
///
/// ## Returns value
/// New object of type [`FrequencySpectrum`].
///
/// ## Examples
/// ### Scaling via dynamic closure
/// ```rust
/// use spectrum_analyzer::{samples_fft_to_spectrum, FrequencyLimit};
/// // get data from audio source
/// let samples = vec![0.0, 1.1, 5.5, -5.5];
/// let res = samples_fft_to_spectrum(
/// &samples,
/// 44100,
/// FrequencyLimit::All,
/// Some(&|val, info| val - info.min),
/// );
/// ```
/// ### Scaling via static function
/// ```rust
/// use spectrum_analyzer::{samples_fft_to_spectrum, FrequencyLimit};
/// use spectrum_analyzer::scaling::scale_to_zero_to_one;
/// // get data from audio source
/// let samples = vec![0.0, 1.1, 5.5, -5.5];
/// let res = samples_fft_to_spectrum(
/// &samples,
/// 44100,
/// FrequencyLimit::All,
/// Some(&scale_to_zero_to_one),
/// );
/// ```
///
/// ## Panics
/// * When `samples.len() > 4096` and `microfft` is used (restriction by the crate)
pub fn samples_fft_to_spectrum(
samples: &[f32],
sampling_rate: u32,
frequency_limit: FrequencyLimit,
scaling_fn: Option<SpectrumScalingFunction>,
) -> Result<FrequencySpectrum, SpectrumAnalyzerError> {
// everything below two samples is unreasonable
if samples.len() < 2 {
return Err(SpectrumAnalyzerError::TooFewSamples);
}
// do several checks on input data
if samples.iter().any(|x| x.is_nan()) {
return Err(SpectrumAnalyzerError::NaNValuesNotSupported);
}
if samples.iter().any(|x| x.is_infinite()) {
return Err(SpectrumAnalyzerError::InfinityValuesNotSupported);
}
if !is_power_of_two(samples.len()) {
return Err(SpectrumAnalyzerError::SamplesLengthNotAPowerOfTwo);
}
let max_detectable_frequency = sampling_rate as f32 / 2.0;
// verify frequency limit: unwrap error or else ok
let _ = frequency_limit
.verify(max_detectable_frequency)
.map_err(SpectrumAnalyzerError::InvalidFrequencyLimit)?;
// With FFT we transform an array of time-domain waveform samples
// into an array of frequency-domain spectrum samples
// https://www.youtube.com/watch?v=z7X6jgFnB6Y
// FFT result has same length as input
// (but when we interpret the result, we don't need all indices)
// applies the f32 samples onto the FFT algorithm implementation
// chosen at compile time (via Cargo feature).
// If a complex FFT implementation was chosen, this will internally
// transform all data to Complex numbers.
let buffer = FftImpl::fft_apply(samples);
// This function:
// 1) calculates the corresponding frequency of each index in the FFT result
// 2) filters out unwanted frequencies
// 3) calculates the magnitude (absolute value) at each frequency index for each complex value
// 4) optionally scales the magnitudes
// 5) collects everything into the struct "FrequencySpectrum"
fft_result_to_spectrum(
samples.len(),
&buffer,
sampling_rate,
frequency_limit,
scaling_fn,
)
}
/// Transforms the FFT result into the spectrum by calculating the corresponding frequency of each
/// FFT result index and optionally calculating the magnitudes of the complex numbers if a complex
/// FFT implementation is chosen.
///
/// ## Parameters
/// * `samples_len` Length of samples. This is a dedicated field because it can't always be
/// derived from `fft_result.len()`. There are for example differences for
/// `fft_result.len()` in real and complex FFT.
/// * `fft_result` Result buffer from FFT. Has the same length as the samples array.
/// * `sampling_rate` sampling_rate, e.g. `44100 [Hz]`
/// * `frequency_limit` Frequency limit. See [`FrequencyLimit´]
/// * `scaling_fn` See [`crate::scaling::SpectrumScalingFunction`].
///
/// ## Return value
/// New object of type [`FrequencySpectrum`].
#[inline(always)]
fn fft_result_to_spectrum(
samples_len: usize,
fft_result: &[Complex32],
sampling_rate: u32,
frequency_limit: FrequencyLimit,
scaling_fn: Option<SpectrumScalingFunction>,
) -> Result<FrequencySpectrum, SpectrumAnalyzerError> {
let maybe_min = frequency_limit.maybe_min();
let maybe_max = frequency_limit.maybe_max();
let frequency_resolution = fft_calc_frequency_resolution(sampling_rate, samples_len as u32);
// collect frequency => frequency value in Vector of Pairs/Tuples
let frequency_vec = fft_result
.iter()
// See https://stackoverflow.com/a/4371627/2891595 for more information as well as
// https://www.gaussianwaves.com/2015/11/interpreting-fft-results-complex-dft-frequency-bins-and-fftshift/
//
// The indices 0 to N/2 (inclusive) are usually the most relevant. Although, index
// N/2-1 is declared as the last useful one on stackoverflow (because in typical applications
// Nyquist-frequency + above are filtered out), we include everything here.
// with 0..=(samples_len / 2) (inclusive) we get all frequencies from 0 to Nyquist theorem.
//
// Indices (samples_len / 2)..len() are mirrored/negative. You can also see this here:
// https://www.gaussianwaves.com/gaussianwaves/wp-content/uploads/2015/11/realDFT_complexDFT.png
.take(FftImpl::fft_relevant_res_samples_count(samples_len))
// to (index, fft-result)-pairs
.enumerate()
// calc index => corresponding frequency
.map(|(fft_index, fft_result)| {
(
// Calculate corresponding frequency of each index of FFT result.
//
// Explanation for the algorithm:
// https://stackoverflow.com/questions/4364823/
//
// N complex samples : [0], [1], [2], [3], ... , ..., [2047] => 2048 samples for example
// (Or N real samples packed
// into N/2 complex samples
// (real FFT algorithm))
// Complex FFT Result : [0], [1], [2], [3], ... , ..., [2047]
// Relevant part of FFT Result: [0], [1], [2], [3], ... , [1024] => indices 0 to N/2 (inclusive) are important
// ^ ^
// Frequency : 0Hz, .................... Sampling Rate/2 => "Nyquist frequency"
// 0Hz is also called (e.g. 22050Hz for 44100Hz sampling rate)
// "DC Component"
//
// frequency step/resolution is for example: 1/2048 * 44100 = 21.53 Hz
// 2048 samples, 44100 sample rate
//
// equal to: 1.0 / samples_len as f32 * sampling_rate as f32
fft_index as f32 * frequency_resolution,
// in this .map() step we do nothing with this yet
fft_result,
)
})
// #######################
// ### BEGIN filtering: results in lower calculation and memory overhead!
// check lower bound frequency (inclusive)
.filter(|(fr, _fft_result)| {
if let Some(min_fr) = maybe_min {
// inclusive!
// attention: due to the frequency resolution, we do not necessarily hit
// exactly the frequency, that a user requested
// e.g. 1416.8 < limit < 1425.15
*fr >= min_fr
} else {
true
}
})
// check upper bound frequency (inclusive)
.filter(|(fr, _fft_result)| {
if let Some(max_fr) = maybe_max {
// inclusive!
// attention: due to the frequency resolution, we do not necessarily hit
// exactly the frequency, that a user requested
// e.g. 1416.8 < limit < 1425.15
*fr <= max_fr
} else {
true
}
})
// ### END filtering
// #######################
// FFT result is always complex: calc magnitude
// sqrt(re*re + im*im) (re: real part, im: imaginary part)
.map(|(fr, complex_res)| (fr, complex_to_magnitude(complex_res)))
// transform to my thin convenient orderable f32 wrappers
.map(|(fr, val)| (Frequency::from(fr), FrequencyValue::from(val)))
// collect all into an sorted vector (from lowest frequency to highest)
.collect::<Vec<(Frequency, FrequencyValue)>>();
// create spectrum object
let spectrum = FrequencySpectrum::new(frequency_vec, frequency_resolution);
// optionally scale
if let Some(scaling_fn) = scaling_fn {
spectrum.apply_scaling_fn(scaling_fn)?
}
Ok(spectrum)
}
/// Calculate the frequency resolution of the FFT. It is determined by the sampling rate
/// in Hertz and N, the number of samples given into the FFT. With the frequency resolution,
/// we can determine the corresponding frequency of each index in the FFT result buffer.
///
/// For "real FFT" implementations
///
/// ## Parameters
/// * `samples_len` Number of samples put into the FFT
/// * `sampling_rate` sampling_rate, e.g. `44100 [Hz]`
///
/// ## Return value
/// Frequency resolution in Hertz.
///
/// ## More info
/// * https://www.researchgate.net/post/How-can-I-define-the-frequency-resolution-in-FFT-And-what-is-the-difference-on-interpreting-the-results-between-high-and-low-frequency-resolution
/// * https://stackoverflow.com/questions/4364823/
#[inline(always)]
fn fft_calc_frequency_resolution(sampling_rate: u32, samples_len: u32) -> f32 {
sampling_rate as f32 / samples_len as f32
}
/// Maps a [`Complex32`] to it's magnitude as `f32`. This is done
/// by calculating `sqrt(re*re + im*im)`. This is required to convert
/// the complex FFT result back to real values.
///
/// ## Parameters
/// * `val` A single value from the FFT output buffer of type [`Complex32`].
fn complex_to_magnitude(val: &Complex32) -> f32 {
// calculates sqrt(re*re + im*im), i.e. magnitude of complex number
let sum = val.re * val.re + val.im * val.im;
let sqrt = libm::sqrtf(sum);
debug_assert!(!sqrt.is_nan(), "sqrt is NaN!");
sqrt
}
// idea from https://stackoverflow.com/questions/600293/how-to-check-if-a-number-is-a-power-of-2
const fn is_power_of_two(num: usize) -> bool {
num != 0 && ((num & (num - 1)) == 0)
}
// tests module for small unit tests
#[cfg(test)]
mod tests2 {
use super::*;
#[test]
fn test_is_power_of_two() {
assert!(!is_power_of_two(0));
assert!(is_power_of_two(1));
assert!(is_power_of_two(2));
assert!(!is_power_of_two(3));
assert!(is_power_of_two(2));
assert!(is_power_of_two(256));
}
}