8Peak-picking — SPEAK & ACE
CIS drives every channel every cycle. The strategies most widely used today take a different tack: analyse the sound into many bands, but in each cycle stimulate only the few bands carrying the most energy — the spectral peaks. This 'n-of-m' approach concentrates the implant's limited stimulation where the information is, and by leaving the quiet channels silent it sidesteps some of the channel interaction that plagues full-channel stimulation. SPEAK pioneered the idea; its faster descendant ACE became the default strategy in Nucleus devices and one of the most-used coding strategies in the world. This module explains how peak-picking works, why it helps, and what the clinician is actually choosing when they set the number of maxima and the stimulation rate.
TStimulate the peaks
An n-of-m strategy analyses sound into n frequency bands but, each stimulation cycle, selects and stimulates only the m bands with the highest energy — the spectral peaks— leaving the rest silent that cycle. As the sound changes, which bands are “in” changes with it, tracking the moving peaks of speech.
CSPEAK and ACE
SPEAK (Spectral Peak) introduced peak-picking with a relatively low, adaptive rate. ACE (Advanced Combination Encoder) kept the n-of-m principle but ran at a much higher stimulation rate, combining SPEAK's spectral selection with CIS-like temporal sampling. ACE became the default strategy in Cochlear's Nucleus devices and is among the most widely used strategies worldwide; MED-EL and Advanced Bionics offer their own CIS- and n-of-m-derived equivalents.[2015]
CWhy pick peaks
Peak-picking helps for two reasons. It puts the limited stimulation budget on the channels that matter — spectral peaks carry most of the speech information — and by not driving every channel at once it reduces channel interaction (Module 7). The silent channels are mostly carrying noise or low-energy detail the listener would not resolve anyway, so little is lost and interaction is gained back.
CThe knobs: m and rate
Two parameters define the trade-off. The number of maxima m sets how much spectral detail is delivered each cycle — more maxima means richer spectrum but more interaction; the stimulation rate sets how finely the temporal envelope is sampled. Higher rates and more maxima are not always better; the best settings vary between recipients, which is part of why programming is individualised rather than fixed.
What is the rationale for peak-picking?
How does an n-of-m strategy such as ACE work?
Why does peak-picking help despite leaving some channels silent each cycle?