Resonate: A Low-Latency, Low-Memory, Low-Cost Spectral Analysis Algorithm

Resonate is a low-latency, low-memory footprint, and low-computational-cost algorithm for evaluating perceptually relevant spectral information from audio (and other) signals. It builds on a resonator model using Exponentially Weighted Moving Average (EWMA) to accumulate signal contributions around resonant frequencies. Its compact iterative formulation allows for efficient updates with minimal arithmetic operations per sample, requiring no buffering. Resonate computes real-time perceptually relevant spectral content estimates; memory and per-sample computational complexity scale linearly with the number of resonators, independent of input sample count. Open-source implementations in Python, C++, and Swift are available, along with demonstration apps.