KFR

KFR, a high-performance C++ Digital Signal Processing (DSP) framework, offers essential building blocks for DSP, audio processing, scientific applications, and beyond. Designed with modern C++ principles, KFR harnesses SIMD (Single Instruction, Multiple Data) instruction sets including SSE, AVX, AVX-512, ARM NEON, and RISC-V RVV, ensuring exceptional efficiency and speed.

Key Features

Efficient DSP Operations

• FFT/DFT: Optimized implementations for discrete Fourier transforms (DFT), supporting non-power-of-two sizes and multidimensional transforms. Performance benchmarks reveal competitive results, ideal for applications that demand high efficiency.
• IIR Filter Design: Create filters using various designs including elliptic, Butterworth, and Chebyshev, including support for zero-phase IIR filtering.
• Sample Rate Conversion: Achieve high-quality audio conversions with configurable linear phase options.

Comprehensive Audio Support

• Audio Encoding/Decoding: Support for a wide range of audio formats such as WAV, AIFF, FLAC, MP3 (decoding), and more, including advanced options like MediaFoundation on Windows for AAC and other formats.
• Universal Compatibility: Binaries available for macOS (Intel and Apple Silicon) and basic RISC-V support on Linux.

Multiarchitecture Capabilities

KFR's architecture-aware design allows for dynamic runtime dispatch, enabling optimized execution across various CPU architectures. This includes specialized support for algorithms such as DFT, FIR, and IIR filters, tailored to the capabilities of the target machine.

Enhanced Mathematical Functions

KFR incorporates a range of mathematical operations optimized for SIMD execution, granting flexibility in handling vectors of different lengths and types. This includes implementations for common mathematical functions such as sin, cos, and log.

User Documentation and Community

Access a wealth of documentation through the official KFR docs site, providing comprehensive guides and examples to maximize the use of the framework. Users are encouraged to explore benchmark results, which provide insights into the performance of KFR against other DSP libraries.

For further details, including benchmark results and installation guidelines, visit the official documentation at KFR Documentation.

KFR continues to evolve, with the latest updates introducing elliptic filter designs, performance improvements, and extensive support for modern C++ standards. The project is actively developed and encourages contributions from the community.