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.
- 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