We describe a new signal processing technique for cochlear implants using a psychoacoustic-masking model. The technique is based on the principle of a so-called "NofM" strategy. These strategies stimulate fewer channels ( ) per cycle than active electrodes (NofM; ). In "NofM" strategies such as ACE or SPEAK, only the channels with higher amplitudes are stimulated. The new strategy is based on the ACE strategy but uses a psychoacoustic-masking model in order to determine the essential components of any given audio signal. This new strategy was tested on device users in an acute study, with either 4 or 8 channels stimulated per cycle. For the first condition (4 channels), the mean improvement over the ACE strategy was . For the second condition (8 channels), no significant difference was found between the two strategies.

Cochlear Implants (CIs) are medical implantable devices that can restore the sense of hearing in people with profound hearing loss. Clinical trials assessing speech intelligibility in CI users have found large intersubject variability. One possibility to explain the variability is the individual differences in the interface created between electrodes of the CI and the auditory nerve. In order to understand the variability, models of the voltage distribution of the electrically stimulated cochlea may be useful. With this purpose in mind, we developed a parametric model that can be adapted to each CI user based on landmarks from individual cone beam computed tomography (CBCT) scans of the cochlea before and after implantation. The conductivity values of each cochlea compartment as well as the weighting factors of different grounding modes have also been parameterized. Simulations were performed modeling the cochlea and electrode positions of 12 CI users. Three models were compared with different levels of detail: a homogeneous model (HM), a non-patient-specific model (NPSM), and a patient-specific model (PSM). The model simulations were compared with voltage distribution measurements obtained from the backward telemetry of the 12 CI users. Results show that the PSM produces the lowest error when predicting individual voltage distributions. Given a patient-specific geometry and electrode positions, we show an example on how to optimize the parameters of the model and how to couple it to an auditory nerve model. The model here presented may help to understand speech performance variability and support the development of new sound coding strategies for CIs.

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Unbalanced bipolar stimulation, delivered using charge balanced pulses, was used to produce “Phantom stimulation”, stimulation beyond the most apical contact of a cochlear implant’s electrode array. The Phantom channel was allocated audio frequencies below 300Hz in a speech coding strategy, conveying energy some two octaves lower than the clinical strategy and hence delivering the fundamental frequency of speech and of many musical tones. A group of 12 Advanced Bionics cochlear implant recipients took part in a chronic study investigating the fitting of the Phantom strategy and speech and music perception when using Phantom. The evaluation of speech in noise was performed immediately after fitting Phantom for the first time (Session 1) and after one month of take-home experience (Session 2). A repeated measures of analysis of variance (ANOVA) within factors strategy (Clinical, Phantom) and interaction time (Session 1, Session 2) revealed a significant effect for the interaction time and strategy. Phantom obtained a significant improvement in speech intelligibility after one month of use. Furthermore, a trend towards a better performance with Phantom (48%) with respect to F120 (37%) after 1 month of use failed to reach significance after type 1 error correction. Questionnaire results show a preference for Phantom when listening to music, likely driven by an improved balance between high and low frequencies.

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Spectral smearing causes, at least partially, that cochlear implant (CI) users require a higher signal-to-noise ratio to obtain the same speech intelligibility as normal hearing listeners. A spectral contrast enhancement (SCE) algorithm has been designed and evaluated as an additional feature for a standard CI strategy. The algorithm keeps the most prominent peaks within a speech signal constant while attenuating valleys in the spectrum. The goal is to partly compensate for the spectral smearing produced by the limited number of stimulation electrodes and the overlap of electrical fields produced in CIs. Twelve CI users were tested for their speech reception threshold (SRT) using the standard CI coding strategy with and without SCE. No significant differences in SRT were observed between conditions. However, an analysis of the electrical stimulation patterns shows a reduction in stimulation current when using SCE. In a second evaluation, 12 CI users were tested in a similar configuration of the SCE strategy with the stimulation being balanced between the SCE and the non-SCE variants such that the loudness perception delivered by the strategies was the same. Results show a significant improvement in SRT of 0.57 dB (p < 0.0005) for the SCE algorithm.

Music perception remains rather poor for many Cochlear Implant (CI) users due to the users' deficient pitch perception. However, comprehensible vocals and simple music structures are well perceived by many CI users. In previous studies researchers re-mixed songs to make music more enjoyable for them, favoring the preferred music elements (vocals or beat) attenuating the others. However, mixing music requires the individually recorded tracks (multitracks) which are usually not accessible. To overcome this limitation, Source Separation (SS) techniques are proposed to estimate the multitracks. These estimated multitracks are further re-mixed to create more pleasant music for CI users. However, SS may introduce undesirable audible distortions and artifacts. Experiments conducted with CI users (N = 9) and normal hearing listeners (N = 9) show that CI users can have different mixing preferences than normal hearing listeners. Moreover, it is shown that CI users' mixing preferences are user dependent. It is also shown that SS methods can be successfully used to create preferred re-mixes although distortions and artifacts are present. Finally, CI users' preferences are used to propose a benchmark that defines the maximum acceptable levels of SS distortion and artifacts for two different mixes proposed by CI users.