UC Irvine

Different from traditional Fourier analysis, a signal can be decomposed into amplitude and frequency modulation components. The speech processing strategy in most modern cochlear implants only extracts and encodes amplitude modulation in a limited number of frequency bands. While amplitude modulation encoding has allowed cochlear implant users to achieve good speech recognition in quiet, their performance in noise is severely compromised. Here, we propose a novel speech processing strategy that encodes both amplitude and frequency modulations in order to improve cochlear implant performance in noise. By removing the center frequency from the subband signals and additionally limiting the frequency modulation's range and rate, the present strategy transforms the fast-varying temporal fine structure into a slowly varying frequency modulation signal. As a first step, we evaluated the potential contribution of additional frequency modulation to speech recognition in noise via acoustic simulations of the cochlear implant. We found that while amplitude modulation from a limited number of spectral bands is sufficient to support speech recognition in quiet, frequency modulation is needed to support speech recognition in noise. In particular, improvement by as much as 71 percentage points was observed for sentence recognition in the presence of a competing voice. The present result strongly suggests that frequency modulation be extracted and encoded to improve cochlear implant performance in realistic listening situations. We have proposed several implementation methods to stimulate further investigation.