More about FFT/DFT

Fast Fourier Transform (FFT) can be used to perform:

• Convolution (including convolution reverberation)
• Cross-correlation and auto-correlation
• Applying large FIR filters
• Sample rate conversion
• Spectrum visualization
• Large integer multiplication
• Wavelet transform
• and many other algorithms

Often FFT is the most efficient way to perform each of these algorithms.

About KFR DFT implementation

KFR implementation of the FFT:

• is fully optimized for X86, X86-64, ARM and AARCH64 processors
• uses vector intrinsics (if available for cpu)
• supports both single- and double precision
• can cache internal data between calls to speed up plan creation
• can do forward and inverse FFT without a need to create two plans
• can be used for complex-to-complex, real-to-complex and complex-to-real 1D transforms
• doesn’t require measure FFT performance at runtime and to find an optimal configuration
• has special implementations for FFT sizes up to 256
• has no external dependencies
• is thread-safe, no global data
• is written in modern C++14
• is open source (GPL v2+ license)