A low-power asynchronous interleaved sampling algorithm for cochlear implants that encodes envelope and phase information.
Cochlear implants currently fail to convey phase information, which is important for perceiving music, tonal languages, and for hearing in noisy environments. We propose a bio-inspired asynchronous interleaved sampling (AIS) algorithm that encodes both envelope and phase information, in a manner that may be suitable for delivery to cochlear implant users. Like standard continuous interleaved sampling (CIS) strategies, AIS naturally meets the interleaved-firing requirement, which is to stimulate only one electrode at a time, minimizing electrode interactions. The majority of interspike intervals are distributed over 1-4 ms, thus staying within the absolute refractory limit of neurons, and form a more natural, pseudostochastic pattern of firing due to complex channel interactions. Stronger channels are selected to fire more often but the strategy ensures that weaker channels are selected to fire in proportion to their signal strength as well. The resulting stimulation rates are considerably lower than those of most modern implants, saving power yet delivering higher potential performance. Correlations with original sounds were found to be significantly higher in AIS reconstructions than in signal reconstructions using only envelope information. Two perceptual tests on normal-hearing listeners verified that the reconstructed signals enabled better melody and speech recognition in noise than those processed using tone-excited envelope-vocoder simulations of cochlear implant processing. Thus, our strategy could potentially save power and improve hearing performance in cochlear implant users.
Sit
Ji-Jon
JJ
Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
Simonson
Andrea M
AM
Oxenham
Andrew J
AJ
Faltys
Michael A
MA
Sarpeshkar
Rahul
R
D000465:Algorithms; D003054:Cochlear Implants; D004867:Equipment Design; D019544:Equipment Failure Analysis; D016247:Information Storage and Retrieval / Q000379:methods*; D013018:Sound Spectrography / Q000295:instrumentation* / Q000379:methods*; D049250:Speech Recognition Software; D013813:Therapy, Computer-Assisted / Q000295:instrumentation / Q000379:methods*