A simple and efficient Fast Fourier Transform (FFT) library.

FFT converts a time domain signal to the frequency domain, and back again. This is useful for all sorts of applications:

• Filtering or synthesizing audio
• Compression algorithms such as JPEG and MP3
• Computing a spectrogram (most AI applications that analyze audio actually do visual analysis of the spectrogram)
• Convolutions, such as reverb filters for audio, or blurring filters for images

This library supports FFT of real or complex arrays of any size. It also includes some related utilities, such as windowing functions, STFT, inverse FFT, and circular and linear convolution.

Running a basic real-valued FFT:

import 'package:fftea/fftea.dart';

List<double> myData = ...;

final fft = FFT(myData.length);
final freq = fft.realFft(myData);

freq is a Float64x2List representing a list of complex numbers. See ComplexArray for helpful extension methods on Float64x2List.

Running an STFT to calculate a spectrogram:

import 'package:fftea/fftea.dart';

List<double> audio = ...;

final chunkSize = 1234;
final stft = STFT(chunkSize, Window.hanning(chunkSize));

final spectrogram = <Float64List>[];
stft.run(audio, (Float64x2List freq) {
  spectrogram.add(freq.discardConjugates().magnitudes());
});

The result of a real valued FFT is about half redundant data, so discardConjugates removes that data from the result (a common practice for spectrograms):

[sum term, ...terms..., nyquist term, ...conjugate terms...]
 ^----- These terms are kept ------^     ^- Discarded -^

Then magnitudes discards the phase data of the complex numbers and just keeps the amplitudes, which is usually what you want for a spectrogram.

If you want to know the frequency of one of the elements of the spectrogram, use stft.frequency(index, samplingFrequency), where samplingFrequency is the frequency that audio was recorded at, eg 44100. The maximum frequency (aka nyquist frequency) of the spectrogram will be stft.frequency(chunkSize / 2, samplingFrequency).

See the example for more detailed usage.

If you need to squeeze every last CPU cycle out of your FFT, try these tips:

• Construct your FFT object once, and reuse it, rather than constructing it each time you want to do an FFT.
• Avoid unnecessary array allocations. If you need to do many FFTs, try reusing the same input array, and just overwrite the data. All the FFT methods have documentation stating whether they allocate any new arrays.
• If you're doing something like: input data source -> manually convert to a List<double> -> pass to FFT.realFft, this is a bit inefficient. Instead do: input data source -> manually convert to a Float64x2List -> pass to FFT.inPlaceFft. This eliminates an array alocation and conversion.
• Try to use power of two sized arrays. For example, if you're computing a spectrogram, you can usually just round up the chunk size to the next power of two. Or if you're filtering some audio, try padding your input with silence until it's a power of two, and then trimming the silence afterwards.
• Try to use smaller FFTs if possible. For example, if you're doing STFT, smaller chunk sizes are faster. It's faster to do two FFTs of size N than one FFT of size 2N.

Fast Fourier Transform isn't really a single algorithm. It's a family of algorithms that all try to perform the Discreet Fourier Transform (DFT) in O(n*log(n)) time (a naive implementation of DFT is O(n^2)), where n is the size of the input array. These algorithms differ mainly by what kind of array sizes they can handle. For example, some FFT algorithms can only handle power of two sized arrays, while others can only handle prime number sized arrays.

Most FFT libraries only support power of two arrays. The reason for this is that it's relatively easy to implement an efficient FFT algorithm for power of two sizes arrays, using the Cooley-Tukey algorithm. To handle all array sizes, you need a patchwork of different implementations for different kinds of sizes.

In general, Cooley-Tukey algorithm actually works for any non-prime array size, by breaking it down into its prime factors. Powers of two just happen to be particularly fast and easy to implement.

This library handles arbitrary sizes by using Cooley-Tukey to break the size into its prime factors, and then using Rader's algorithm to handle large prime factors, or a naive O(n^2) implementation which is faster for small factors.

There's also a special Radix2FFT implementation for power of two FFTs that is much faster than the other implementations.

Rader's algorithm handles a prime numbered size, n, by transforming it into an FFT of size n - 1, which is non-prime, so can be handled by Cooley-Tukey. Alternatively, the n - 1 FFT can be zero padded up to a power two, which is usually faster because the special Radix2FFT can be used. See the primePaddingHeuristic function.

There are also a few special implementations for handling fixed sized FFTs of very small sizes. There's never really a practical use case for an FFT of size 2 or 3, but these form the base cases of Cooley-Tukey, and make larger FFTs much faster.

Check out the various implementations of FFT in lib/impl.dart for more details.

This package was built because package:fft is not actively maintained anymore, isn't very efficient, and only supports power of two FFTs. This package aims to support FFTs of any size efficiently, and to make power of two FFTs particularly fast. There are a few improvements that make this Radix2FFT implementation efficient:

• The main FFT class is constructed with a given size, so that the twiddle factors only have to be calculated once. This is particularly handy for STFT.
• Doesn't use a wrapper class for complex numbers, just uses a Float64x2List. Every little wrapper class is a seperate allocation and dereference. For inner loop code, like FFT's complex number math, this makes a big difference. Float64x2 can also take advantage of SIMD optimisations.
• The FFT algorithm is in-place, so no additional arrays are allocated.
• Loop based FFT, rather than recursive.
• Using trigonometric tricks to calculate fewer twiddle factors.
• Bithacks
• FFT implementations are cached by size.

I found some other promising Dart FFT implementations, so I decided to benchmark them too: scidart, and smart_signal_processing. package:fft and smart_signal_processing only support power of two arrays. Scidart supports arrays of any size, but for other sizes they use naive DFT, which is much slower. So for this first set of benchmarks I'm just testing power of two sizes.

To run the benchmarks:

1. Go to the bench directory and pub get, cd bench && dart pub get
2. Run dart run bench.dart

fftea gains some of its speed by caching the twiddle factors between runs, and by doing the FFT in-place. So the benchmarks are broken out into cached and in-place variants. Caching and running in-place is also applied to some of the other libraries, where appropriate.

In the table below, "cached" means the construction of the FFT object is not included in the benchmark time. And "in-place" means using the in-place FFT, and the conversion and copying of the input data into whatever format the FFT wants is not included in the benchmark. Run in Ubuntu on a Dell XPS 13.

In practice, you usually know how big your FFT is ahead of time, so it's pretty easy to construct your FFT object once, to take advantage of the caching. It's sometimes possible to take advantage of the in-place speed up too, for example if you have to copy your data from another source anyway you may as well construct the flat complex array yourself. Since this isn't always possible, the "fftea, cached" times are probably the most representative. In that case, fftea is about 60-80x faster than package:fft, and about 70% faster than smart_signal_processing. Not sure what's going on with scidart, but it seems to be O(n^2) even for power of two sizes.

* Scidart's FFT doesn't have an in-place mode, but they do use a custom format, so in-place means that the time to convert to that format is not included in the benchmark.

I also benchmarked fftea's various implementations of FFT at different sizes, using bench/impl_bench.dart.

This graph shows how the different implementations perform at different sizes.

This graph shows the performance of the implementation selected by the FFT.FFT constructor, which attempts to automatically pick the right implementation for the given size. Although the overall O(n*log(n)) worst case slope is maintained, there is a lot of variation between specific sizes.

Looking at the first graph, you can see that Radix2FFT is consistently the fastest, the PrimeFFT variants are the slowest, and CompositeFFT is in between. Generally, the more composite the size is, the faster the FFT will be. So if you have some flexibility in the FFT size you can choose, try to choose a highly composite size (ie, one where the prime factors are small), or ideally choose a power of two.