Estimating Pitch Information From Simulated Cochlear Implant Signals With Deep Neural Networks.

Abstract

Cochlear implant (CI) users, even with substantial speech comprehension, generally have poor sensitivity to pitch information (or fundamental frequency, F0). This insensitivity is often attributed to limited spectral and temporal resolution in the CI signals. However, the pitch sensitivity markedly varies among individuals, and some users exhibit fairly good sensitivity. This indicates that the CI signal contains sufficient information about F0, and users' sensitivity is predominantly limited by other physiological conditions such as neuroplasticity or neural health. We estimated the upper limit of F0 information that a CI signal can convey by decoding F0 from simulated CI signals (multi-channel pulsatile signals) with a deep neural network model (referred to as the CI model). We varied the number of electrode channels and the pulse rate, which should respectively affect spectral and temporal resolutions of stimulus representations. The F0-estimation performance generally improved with increasing number of channels and pulse rate. For the sounds presented under quiet conditions, the model performance was at best comparable to that of a control waveform model, which received raw-waveform inputs. Under conditions in which background noise was imposed, the performance of the CI model generally degraded by a greater degree than that of the waveform model. The pulse rate had a particularly large effect on predicted performance. These observations indicate that the CI signal contains some information for predicting F0, which is particularly sufficient for targets under quiet conditions. The temporal resolution (represented as pulse rate) plays a critical role in pitch representation under noisy conditions.