Filterreeglite

Filterreeglite is a lightweight, modular software library designed for real-time data filtering and adaptive signal processing. It provides a collection of filter implementations, streaming interfaces, and utilities that enable low-latency processing of numeric data streams in embedded and desktop environments.

Its name blends filter, reg from regulation or regression, and lite to indicate a compact footprint. The project aims to balance performance with portability, offering straightforward integration points for applications that require deterministic latency and bounded memory use.

Architecture and features: The library defines core abstractions such as Filter, FilterChain, and AdaptationModule. Supported filter types include finite impulse response (FIR) and infinite impulse response (IIR) filters, as well as adaptive algorithms such as least-mean-squares (LMS) and recursive least squares (RLS). A regularization-aware mode provides stability in ill-conditioned configurations. The API emphasizes streaming operation, zero-copy buffers, and thread-safe execution.

Platform and usage: Filterreeglite is implemented in C++ with optional Python bindings. It runs on Windows, Linux, and macOS, and can leverage GPU acceleration for large-scale filtering tasks if available. Typical usage involves constructing a filter graph or chain, supplying input samples, and reading filtered outputs in a deterministic fashion suitable for real-time systems.

Performance and limitations: The library is designed for low latency, with latency typically in the sub-millisecond to a few milliseconds range depending on filter order and hardware. Memory usage is controlled via fixed-size buffers and explicit deallocation; users should consider worst-case paths when employing highly adaptive algorithms.

Development and licensing: Filterreeglite is open source under a permissive license. Development occurs on a public repository with issue tracking, continuous integration, and thorough documentation. Community contributions are welcome.

See also: Digital filter, Adaptive filter, Real-time data processing.