Rconvout

Rconvout is a framework for computing recursive convolutions, providing efficient, low-latency convolution of streaming signals with finite impulse response kernels. It combines the efficiency of recursive feedback with the flexibility of convolution, enabling real-time processing without re-evaluating large kernels for every sample.

Origin and development: The concept emerged in the signal processing literature during the 2010s as researchers sought to reduce the computational load of long convolution windows. Rconvout describes expressing the convolution as a recursion over previous outputs and recent inputs, yielding constant-time updates per sample.

Architecture and operation: A typical implementation uses a set of state variables to maintain intermediate sums, transforming the standard convolution into a recursive form. The framework supports multi-channel streams, streaming I/O, and optimizations such as circular buffers and fixed-point arithmetic. Stability and numerical precision are central concerns, with design emphasis on maintaining bounded signals and predictable behavior under finite-precision arithmetic.

Applications and limitations: Rconvout finds use in audio effects, room acoustics modeling, communications equalization, and sensor data analysis, especially for long kernels on resource-limited hardware. Limitations include sensitivity to numerical precision, potential latency tied to kernel length, and the need for careful kernel design to ensure stability.

See also: Convolution; Recursive filter; IIR filter.