KFR

KFR is an open source C++ math framework with focus on DSP.

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

Features

• 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 dispatching
• Multi-versioning. Code for various architecttures (SSE2, AVX2, etc) can co-exist in one translation unit. No need to compile for all cpus

Included DSP/audio algorithms:

• FFT
• FIR filtering
• FIR filter design using the window method
• Resampling with configurable quality (See resampling.cpp from Examples directory)
• Goertzel algorithm
• 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

Performace

FFT (double precision, sizes from 1024 to 16777216)

Prerequisities

• XCode 6.3, 6.4, 7.x, 8.x, or C++14-compliant compiler (currently only Clang 3.7 or newer is supported)
• 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

Installation

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
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

Tests

Execute build.py 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
• Windows 8.1 / clang version 3.8.0 (branches/release_38)

Planned for future versions

• 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

License

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