KFR is an open source C++ DSP framework that focuses on high performance (see benchmark results section).
KFR is a header-only library (except DFT) and has no external dependencies.
- Full AVX-512 support
- EBU R128
- Non-power of two DFT (coming soon)
- TruePeak (coming soon)
- Number of automatic tests has been increased
- GPL version changed from 3 to 2+
- All code in the library is optimized for Intel, AMD (SSE2, SSE3, SSE4.x, AVX and AVX2 and AVX512) and ARM (NEON) 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
- 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
- Simple waveshaper
- Fast incremental sine/cosine generation
- EBU R128
FFT (double precision, sizes from 1024 to 16777216) See fft benchmark for details about benchmarking process.
- CMake 3.0 or newer for building tests and examples
- Python 2.7 or 3.x for building examples
- (Optional) Ninja (https://ninja-build.org/)
For running examples and plotting frequency responses of filters the following python packages are required:
pip install matplotlib
pip install numpy
pip install scipyOr download prebuilt python packages for windows
To obtain the full code, including examples and tests, you can clone the git repository:
git clone https://github.com/kfrlib/kfr.git
CMakeLists.txt contains two libraries:
kfr- header only interface librarykfr_dft- static library for DFT and related algorithms
# Include KFR subdirectory
add_subdirectory(kfr)
# Add header-only KFR to your executable or library, this sets include directories etc
target_link_libraries(your_executable_or_library kfr)
# Add KFR DFT to your executable or library, (cpp file will be built for this)
target_link_libraries(your_executable_or_library kfr_dft)# Add this to command line
-Ipath_to_kfr/include- GCC 5.4 or newer
- Clang 4.0 or newer
cd <path_to_kfr>
mkdir build && cd build
cmake -DENABLE_TESTS=ON -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release ..
make -- -jOr using Ninja
cd <path_to_kfr>
mkdir build && cd build
cmake -GNinja -DENABLE_TESTS=ON -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release ..
ninja- XCode 8.3, 9.x or 10.x
Using Xcode project:
cd <path_to_kfr>
mkdir build && cd build
cmake -GXcode -DENABLE_TESTS=ON -DCMAKE_BUILD_TYPE=Release ..
cmake --build .Using Unix Makefiles:
cd <path_to_kfr>
mkdir build && cd build
cmake -G"Unix Makefiles" -DENABLE_TESTS=ON -DCMAKE_BUILD_TYPE=Release ..
make -- -jOr using Ninja:
cd <path_to_kfr>
mkdir build && cd build
cmake -GNinja -DENABLE_TESTS=ON -DCMAKE_BUILD_TYPE=Release ..
ninja- Visual Studio 2017
- Latest Clang (http://llvm.org/)
- Ninja is highly recommended because Visual Studio does not support parallel build with Clang at this moment.
To work with KFR in Visual Studio you must add the path to the include directory inside KFR directory to the list of the project's include directories.
More details:
https://docs.microsoft.com/en-us/cpp/ide/vcpp-directories-property-page?view=vs-2017
Make sure that LLVM toolset is set for the project
Download and install official LLVM extension:
- LLVM toolchain for Visual Studio https://marketplace.visualstudio.com/items?itemName=LLVMExtensions.llvm-toolchain
More details: https://docs.microsoft.com/en-us/cpp/ide/general-property-page-project?view=vs-2017
LLVM/Clang has very good compatibility with MSVC ABI and it's widely used for building large projects on Windows (including Chrome), so switching to LLVM/Clang should not cause compatibility problems.
Using Ninja:
cd <path_to_kfr>
mkdir build && cd build
call "C:\<path to your Visual Studio installation>\VC\Auxiliary\Build\vcvars64.bat"
cmake -GNinja -DENABLE_TESTS=ON -DCMAKE_CXX_COMPILER="C:/Program Files/LLVM/bin/clang-cl.exe" -DCMAKE_CXX_FLAGS=-m64 -DCMAKE_BUILD_TYPE=Release ..
ninja
Or generate Visual Studio solution (building will be slower): 8000
cd <path_to_kfr>
mkdir build && cd build
cmake -G"Visual Studio 15 2017 Win64" -DENABLE_TESTS=ON -Tllvm -DCMAKE_BUILD_TYPE=Release ..
- Latest MinGW or MSYS2
- Clang 4.0 or newer
Using Makefiles:
cd <path_to_kfr>
mkdir build && cd build
cmake -DENABLE_TESTS=ON -DCMAKE_BUILD_TYPE=Release ..
make -- -j
Using Ninja:
cd <path_to_kfr>
mkdir build && cd build
cmake -GNinja -DENABLE_TESTS=ON -DCMAKE_BUILD_TYPE=Release ..
ninja
Every commit is tested in various OS, compilers, compiler settings etc.
You can run the tests using these commands:
cd <path_to_cmake_build_directory>
cd tests
ctest -VTested on the following systems:
- (Intel AVX2) macOS 10.13.6 / Xcode 10 / AppleClang 10.0.0.10001145
- (Intel AVX Azure Pipelines) macOS 10.13.6 / Xcode 10.1 / AppleClang 10.0.0.10001145
- (Intel AVX Azure Pipelines) macOS 10.13.6 / Xcode 10 / AppleClang 10.0.0.10001145
- (Intel AVX Azure Pipelines) macOS 10.13.6 / Xcode 9.4.1 / AppleClang 9.1.0.9020039
- (Intel AVX Azure Pipelines) macOS 10.13.6 / Xcode 9.0.1 / AppleClang 9.0.0.9000038
- (Intel AVX Azure Pipelines) macOS 10.13.6 / Xcode 8.3.3 / AppleClang 8.1.0.8020042
- (Intel AVX2) macOS 10.11.6 / Xcode 7.3 / AppleClang 7.3.0.7030031
- (Intel AVX2) macOS 10.11.4 / Xcode 7.3 / AppleClang 7.3.0.7030031
- (ARMv7, ARMv7s, ARM64) macOS 10.11.6 / Xcode 7.3 / AppleClang 7.3.0.7030031
- (Intel AVX) macOS 10.10.5 / Xcode 7.1 / AppleClang 7.0.0.7000176
- (SSE4.2 Travis-CI) macOS 10.11.6 / Xcode 8 (beta4) / AppleClang 8.0.0.8000035
- (SSE4.2 Travis-CI) macOS 10.11.5 / Xcode 7.3 / AppleClang 7.3.0.7030031
- (SSE4.2 Travis-CI) macOS 10.11.5 / Xcode 7.2 / AppleClang 7.0.2.7000181
- (SSE4.2 Travis-CI) macOS 10.10.5 / Xcode 7.1 / AppleClang 7.0.0.7000176
- (SSE4.2 Travis-CI) macOS 10.10.5 / Xcode 7 / AppleClang 7.0.0.7000072
- (SSE4.2 Travis-CI) macOS 10.10.5 / Xcode 6.4 / AppleClang 6.1.0.6020053
- (SSE4.2 Travis-CI) macOS 10.10.3 / Xcode 6.3 / AppleClang 6.1.0.6020049
- (Intel AVX2) Ubuntu 18.04 / gcc-7.x / clang version 7.0.0 (tags/RELEASE_700/final)
- (Intel AVX2) Ubuntu 16.04 / gcc-5.4.0 / clang version 3.8.0 (tags/RELEASE_380/final)
- (ARMv7 NEON) Ubuntu 16.04 / gcc-5.4.0 / clang version 3.8.0 (tags/RELEASE_380/final)
- (ARMv7 NEON) Ubuntu 14.04 / gcc-4.8.4 / clang version 3.8.0 (tags/RELEASE_380/final)
- (ARMv7 NEON Travis-CI) Ubuntu 14.04 / gcc-4.8.4 / clang version 3.8.0 (tags/RELEASE_380/final)
- (Intel AVX2 Travis-CI) Ubuntu 12.04 / gcc-5.4.0 / clang version 3.8.0 (tags/RELEASE_380/final)
- (Intel AVX2 Travis-CI) Ubuntu 14.04 / gcc-5.3.0 (Ubuntu 5.3.0-3ubuntu1~14.04) 5.3.0 20151204 / clang version 3.8.0 (tags/RELEASE_380/final)
- (Intel AVX512) Windows 10 / Visual Studio 2017 / Clang 7.0
- (Intel AVX512) Windows 10 / Visual Studio 2017 / Clang 6.0
- (Intel AVX2) Windows 10 / MinGW-W64 5.2 / clang version 3.8.0 (branches/release_38)
- (Intel AVX2) Windows 10 / MinGW-W64 4.8 / clang version 3.8.0 (branches/release_38)
- (Intel AVX) Windows 8.1 / MinGW-W64 5.4 / clang version 3.8.0 (branches/release_38)
- (Intel AVX) Windows 8.1 / Visual Studio 2015 Update 2 / clang version 3.9.0 (SVN r273898 (27 June 2016))
- (Intel AVX2) Windows 10.0.17134.407 compatible with Ubuntu 18.04 / gcc-7.x / clang version 7.0.0 (tags/RELEASE_700/final)
- (Intel AVX2) Windows 10.0.14393 compatible with Ubuntu 14.04 / gcc-5.4.0 / clang version 3.8.0 (tags/RELEASE_380/final)
- 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 2+ license.
If you want to use KFR in commercial product or a closed-source project, you need to purchase a Commercial License