8SMSP: Letting the Spectrum Choose
The Spectral Maxima Sound Processor abandoned explicit feature extraction entirely. It analysed the spectrum with a 16-filter bank and transmitted only the six largest spectral maxima — no F0, no F1, no formant tracking. This data-driven 6-of-16 design is the bridge between the feature-extraction era and the modern n-of-m family.
TThe SMSP principle
SMSP is an n-of-m spectral-maxima strategy (6-of-16) that extracts no explicit features such as F0 or F1 from the waveform It analyses the spectrum with a 16-filter bank and transmits the six largest spectral maxima It was developed in the early 1990s for the Nucleus multi-electrode cochlear implant Unlike MPEAK it makes no assumptions about which spectral peaks are formants.[1993][2006]
CThe 6-of-16 chain
The chain is: speech to a bank of 16 band-pass filters (centre frequencies 250-5400 Hz) to per-band rectification plus low-pass filtering (200 Hz cutoff) to a spectral-maxima detector that selects the six largest of 16 outputs every 4 ms to logarithmic compression to radio transmission to the six selected electrodes The 16 band-pass filters have centre frequencies spanning 250-5400 Hz Per channel the envelope is obtained by rectification and a 200 Hz low-pass filter The six maxima are selected at 4 ms intervals and their amplitudes are logarithmically compressed.[1993][2006]
TRobustness over feature extraction
By selecting the largest spectral peaks rather than estimating formants, SMSP is robust to the formant-extraction errors that degraded MPEAK in noise Typical clinical stimulation rates for SMSP ranged from 250 pps to 1800 pps The pure-spectral approach foreshadowed the n-of-m family (SPEAK, ACE) that became commercially dominant Strategies based on spectral signal analysis outperformed explicit speech-feature extraction overall.[2006][1993]
CWhere SMSP sits in the lineage
SMSP is conceptually the direct ancestor of the n-of-m / spectral-peak family It was superseded in commercial use by higher-rate, more-flexible spectral-maxima strategies (SPEAK and ACE) and by CIS implementations within the Nucleus and other systems The idea of picking n maxima of m bands generalises directly into SPEAK and ACE SMSP demonstrated that throwing away the feature-extraction model improved noise robustness.[2006][1993]
TBy the numbers
Which strategy is being described, and what is its defining advantage over MPEAK?
What is the n-of-m configuration of the SMSP strategy?
What does SMSP deliberately NOT extract that MPEAK did?