Users of cochlear implants rely on a number of electrodes to perceive acoustic information. The extent to which their hearing is restored depends on a number of factors including the electrode-to-neuron interface. We describe an approach to detect instances of poor-performing channels based on physiological data known as electrically evoked compound action potentials (ECAPs). The proposed approach — termed Panoramic ECAP ("PECAP") — combines nonlinear optimization stages with different constraints to recover neural activation patterns for all electrodes. Data were obtained from nine cochlear implant subjects and used to run the PECAP tool to identify possible instances of poor-performing channels. Data from one subject revealed a shifted peak ("dead region").

Cochlear implants (CIs) are devices capable of restoring hearing function in profoundly-deaf patients to an acceptable degree of performance. An essential processing step in any cochlear implant is frequency analysis, which is usually performed via banks of filters. Here, we simulate and test the suitability of different filters and filterbank architectures for CIs with respect to their performance in speech intelligibility. Four different filters were implemented in an established model of CI hearing, the tone-excited vocoder, namely: GTF (Gammatone Filter), DAPGF (Differentiated All-Pole GTF), OZGF (One-Zero GTF) and BUTF (Butterworth). Three filterbank parameters, the filter order ( N), the filter quality factor ( Q) and the number of channels ( Ch), and their combinations were tested using objective and subjective metrics. Simulation results show that all filters tested are suitable for CI implementation, but that the choice of Q and N parameter values is crucial. For most conditions, optimal ( N,Q) combinations were within few units away from the combination (2, 4).

A non-intrusive objective speech quality and intelligibility measure tailored to hearing restoration instruments is proposed and evaluated in complex listening environments. The measure builds upon the previously-proposed “speech-to-reverberation modulation energy ratio” (SRMR) by incorporating hearing impairment percepts, such as hearing loss thresholds and altered modulation frequency selectivity. Performance is assessed using speech data corrupted by additive noise, reverberation, and noise-plus-reverberation which were subjectively rated by cochlear implant and hearing aid users. Experimental results show that the developed measures outperform the original SRMR metric for hearing impaired listeners and achieve performance levels inline with existing intrusive quality and intelligibility metrics, but with the advantage of not requiring access to a clean reference signal. As such, the measure may be used to develop qualityor intelligibility-aware speech enhancement algorithms for advanced hearing restoration instruments.

A measure to predict speech intelligibility in unilateral and bi-lateral cochlear implant (CI) users is proposed that does not need a priori information (i.e. is non-intrusive), such as the room acoustics. Such measure, termed BiSIMCI, combines an equalization-cancellation stage together with a modulation frequency estimation stage. Simulated and actual subjective data from CI users were used to validate the proposed mea-sure. The actual CI subjective data consisted of speech re-ception thresholds (SRTs) collected in anechoic rooms with a total of 28 target/interferer spatial configurations. The simu-lated CI subjective data were generated by running the intrusive algorithm by Culling et al. [Ear & Hearing 33 (6), 673-682 (2012)] across 109 different target/interferer conditions from two environments, one anechoic and one highly reverberant room (RT60 = 0.89s). The experimental results indicate that the proposed non-intrusive measure provides reliable predic-tions when compared with both actual and simulated SRT; an average correlation of 0.94 was reported for these conditions, and an average correlation of 0.97 was obtained when some in-trusive assumptions were made. 1.

In this paper, we evaluate the performance of six intrusive ob-jective measures as intelligibility predictors of degraded speech for cochlear implant (CI) users. Three practical environmental degradation scenarios are considered: reverberation alone, ad-ditive noise alone, and noise-plus-reverberation. A subjective intelligibility test was performed with eleven cochlear implant users and objective measures were evaluated using four per-formance metrics: Pearson, Spearman rank, and sigmoid-fitted correlation coefficients, and the root mean square error. It was observed that existing metrics performed well in the noise-alone scenarios, but obtained lower performance in the reverberation-alone scenario and in many cases, unacceptable results in the noise-plus-reverberation scenario. It is concluded that further work is still needed in order to accurately predict speech intel-ligibility ratings for CI users, particularly in environments cor-rupted by reverberation.