Why KFR
Performance First
KFR functions and algorithms are carefully designed and implemented for maximum performance.
Proven in production
10 years of history. 1929 stars, 284 forks, 33 releases on GitHub. Every pushed commit runs through 200,934,195 checks across 12,735 test cases.
Portable
It is portable across SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2, AVX-512, NEON on ARMv7 and ARMv8 (AArch64), and RISC-V 64 with RVV, and is equally optimized for Intel and AMD CPUs.
FFT performance on AVX2 and NEON. KFR 7.1.0 is shown in red. Higher is better (GFLOPS).
What's inside
Discrete Fourier Transform
Real and complex transforms of any size, including non-power-of-2. Multidimensional FFTs are also supported.
Biquad filters and filter design
Second-order sections for lowpass, highpass, EQ, shelving, cascades, streaming, and DC removal.
IIR filter design
Butterworth, Bessel, Chebyshev, and elliptic prototypes transformed into digital SOS filters.
FIR filters and design
Windowed-sinc filter design, short SIMD filters, runtime streaming filters, and moving sums.
High quality Sample Rate Conversion
Polyphase resampling with configurable quality, linear phase, continuous streams, and delay compensation.
EBU R 128 Loudness
EBU R128-compliant metering for momentary, short-term, integrated loudness, and loudness range.
Window functions
Common analysis and FIR-design windows with symmetric or periodic sampling and configurable parameters.
Mathematical and statistical functions
High-performance mathematical operations, reductions, interpolation, and statistics for scalar and SIMD data.
Audio file reading/writing
Read and write WAV, W64, AIFF, CAF, FLAC, MP3, and raw audio through streaming-friendly APIs.
Built for audio plugins, embedded and real-time processing, scientific computing, ML data pipelines and more.
What Our Customers Say
reFX: Nexus
We use kfrlib with great success. It has ready to use convolution, FFT and other goodies. Builds for x64, arm, etc. with full optimizations and native SIMD usage. We’re using it in Nexus for FFT and convolution.
LIGO, Virgo and KAGRA: Gravitational-wave research
The integration of KFR library, a modern C++ library actively maintained for around a decade, enables ROOT data analysis framework with two agnostic key features: rapid FFT calculations and advanced signal processing. KFR allows efficient handling of large signals through high-performance Fast Fourier Transform (FFT) computations in n-dimensions and also robust signal processing techniques, including windowing functions, Finite Impulse Response (FIR), and Infinite Impulse Response (IIR) filtering.