KFR is an open source C++ DSP framework that focuses on high performance (see benchmark results section).

KFR is a header-only and has no external dependencies.

• All code in the library is optimized for SSE2, SSE3, SSE4.x, AVX and AVX2 processors
• Mathematical and statistical functions
• Template expressions (See examples)
• All data types are supported including complex numbers
• All vector lengths are also supported. vec<float,1>, vec<unsigned,3>, vec<complex<float>, 11> all are valid vector types in KFR
• Most of the standard library functions are re-implemented to support vector of any length and data type
• Runtime cpu detection

Included DSP/audio algorithms:

• FFT
• Convolution
• FIR filtering
• FIR filter design using the window method
• Resampling with configurable quality (See resampling.cpp from Examples directory)
• Goertzel algorithm
• Fractional delay
• Biquad filtering
• Biquad design functions
• Oscillators: Sine, Square, Sawtooth, Triangle
• Window functions: Triangular, Bartlett, Cosine, Hann, Bartlett-Hann, Hamming, Bohman, Blackman, Blackman-Harris, Kaiser, Flattop, Gaussian, Lanczos, Rectangular
• Audio file reading/writing
• Pseudorandom number generator
• Sorting
• Ring (Circular) buffer
• Fast incremental sine/cosine generation

FFT (double precision, sizes from 1024 to 16777216) See fft benchmark for details about benchmarking process.

Biquad performance

• macOS: XCode 6.3, 6.4, 7.x, 8.x
• Windows
  • MinGW 5.2 and Clang 3.7 or newer
  • Visual Studio 2015 update 2 and latest Clang 3.9.0
• Ubuntu: GCC 5.1 and Clang 3.7 or newer
• CoMeta metaprogramming library (already included)

KFR is a header-only so just #include <kfr/math.hpp> to start using it

The following tools are required to build the examples:

• CMake 3.x

To build the tests:

• Testo - C++14 testing micro framework (included)

• Python 2.7 with the following modules:

  • dspplot (included, see Installation)
  • matplotlib
  • numpy
  • scipy

To obtain the full code, including examples and tests, you can clone the git repository:

git clone https://github.com/kfrlib/kfr.git

To be able to run the tests and examples install the following python modules:

pip install matplotlib # or download prebuilt package for windows
pip install numpy # or download prebuilt package for windows
pip install scipy # or download prebuilt package for windows

Install dspplot using python setup.py install inside dspplot directory

Execute build.py or build-cl.py (Visual Studio version) to run the tests or run tests manually from the tests directory

Tested on the following systems:

• OS X 10.11.4 / AppleClang 7.3.0.7030031
• Ubuntu 14.04 / gcc-5 (Ubuntu 5.3.0-3ubuntu1~14.04) 5.3.0 20151204 / clang version 3.8.0 (tags/RELEASE_380/final)
• Windows 8.1 / MinGW-W64 / clang version 3.8.0 (branches/release_38)
• Windows 8.1 / Visual Studio Update 2 / clang version 3.9.0 (SVN r273898 (27 June 2016))

• DFT for any lengths (not only powers of two)
• Parallel execution of algorithms
• Serialization/Deserialization of any expression
• More formats for audio file reading/writing
• Reduce STL dependency

KFR is dual-licensed, available under both commercial and open-source GPL license.

If you want to use KFR in commercial product or a closed-source project, you need to purchase a Commercial License