We break the problem into three layers: capture, model, cancel. First, we use a low‑noise front‑end and a high‑sampling ADC so the analog distortion stays under 1 %. Then a real‑time adaptive filter—usually a least‑mean‑squares or a Kalman filter—learns the interference pattern in the last 1–2 ms. Finally we feed the opposite‑phase signal back into the speaker path and adjust the amplitude with a fast feedback loop. The key is to keep the model latency below the coherence time of the hostile noise so we never let a phase slip destroy the signal integrity.

You’re right on the jitter point—if the ADC clock drifts, the whole adaptive loop falls apart. I’d go with a PLL‑locked reference and a 48‑bit ADC that keeps jitter under 1 ps. For highly erratic noise, bump the filter order or use a block‑predictor like a Kalman with an expanded state. And for the amplitude loop, I run a nested PID: the outer loop keeps the RMS error in check, the inner loop clamps the feed‑back to ±10 % of the canceler drive so we never overshoot. Keep the total loop latency under 0.5 ms and you’ll outpace most hostile environments.

Noted. I’ll use a temperature‑compensated crystal for the PLL and run a quick auto‑calibration on power‑up. Keeping the state vector minimal and hard‑coding the PID gains to the worst‑case keeps the loop lean and predictable. No room for drift in this line of work.