A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants

Headphones over the cochlear-implant sound processor to replace direct audio input

Psychoacoustic stimulus presentation to the cochlear implant via direct audio input (DAI) is no longer possible for many newer sound processors (SPs). This study assessed the feasibility of placing circumaural headphones over the SP. Calibration spectra for loudspeaker, DAI, and headphone modalities were estimated by measuring cochlear-implant electrical output levels for tones presented to SPs on an acoustic manikin. Differences in calibration spectra between modalities arose mainly from microphone-response characteristics (high-frequency differences between DAI and the other modalities) or a proximity effect (low-frequency differences between headphones and loudspeaker). Calibration tables are provided to adjust for differences between the three modalities.

A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants

Cochlear implants (CIs) provide a solution for individuals with severe sensorineural hearing loss to regain their hearing abilities. When someone experiences this form of hearing impairment in both ears, they may be equipped with two separate CI devices, which will typically further improve the CI benefits. This spatial hearing is particularly crucial when tackling the challenge of understanding speech in noisy environments, a common issue CI users face. Currently, extensive research is dedicated to developing algorithms that can autonomously filter out undesired background noises from desired speech signals. At present, some research focuses on achieving end-to-end denoising, either as an integral component of the initial CI signal processing or by fully integrating the denoising process into the CI sound coding strategy. This work is presented in the context of bilateral CI (BiCI) systems, where we propose a deep-learning-based bilateral speech enhancement model that shares information between both hearing sides. Specifically, we connect two monaural end-to-end deep denoising sound coding techniques through intermediary latent fusion layers. These layers amalgamate the latent representations generated by these techniques by multiplying them together, resulting in an enhanced ability to reduce noise and improve learning generalization. The objective instrumental results demonstrate that the proposed fused BiCI sound coding strategy achieves higher interaural coherence, superior noise reduction, and enhanced predicted speech intelligibility scores compared to the baseline methods. Furthermore, our speech-in-noise intelligibility results in BiCI users reveal that the deep denoising sound coding strategy can attain scores similar to those achieved in quiet conditions.

A Deep Denoising Sound Coding Strategy for Cochlear Implants

Cochlear implants (CIs) have proven to be successful at restoring the sensation of hearing in people who suffer from profound sensorineural hearing loss. CI users generally achieve good speech understanding in quiet acoustic conditions. However, their ability to understand speech degrades drastically when background interfering noise is present. To address this problem, current CI systems are delivered with front-end speech enhancement modules that can aid the listener in noisy environments. However, these only perform well under certain noisy conditions, leaving quite some room for improvement in more challenging circumstances. In this work, we propose replacing the CI sound coding strategy with a deep neural network (DNN) that performs end-to-end speech denoising by taking the raw audio as input and providing a denoised electrodogram, i.e., the electrical stimulation patterns applied to the electrodes across time. We specifically introduce a DNN that emulates a common CI sound coding strategy, the advanced combination encoder (ACE). We refer to the proposed algorithm as 'Deep ACE'. Deep ACE is designed not only to accurately code the acoustic signals in the same way that ACE would but also to automatically remove unwanted interfering noises, without sacrificing processing latency. The model was optimized using a CI-specific loss function and evaluated using objective measures as well as listening tests in CI participants. Results show that, based on objective measures, the proposed model achieved higher scores when compared to the baseline algorithms. Also, the proposed deep learning-based sound coding strategy gave eight CI users the highest speech intelligibility scores.

Factors Affecting the Accessibility of Voice Telephony for People with Hearing Loss: Audio Encoding, Network Impairments, Video and Environmental Noise

This paper describes four studies with a total of 114 individuals with hearing loss and 12 hearing controls that investigate the impact of audio quality parameters on voice telecommunications. These studies were first informed by a survey of 439 individuals with hearing loss on their voice telecommunications experiences. While voice telephony was very important, with high usage of wireless mobile phones, respondents reported relatively low satisfaction with their hearing devices’ performance for telephone listening, noting that improved telephone audio quality was a significant need. The studies cover three categories of audio quality parameters: (1) narrowband (NB) versus wideband (WB) audio; (2) encoding audio at varying bit rates, from typical rates used in today's mobile networks to the highest quality supported by these audio codecs; and (3) absence of packet loss to worst-case packet loss in both mobile and VoIP networks. Additionally, NB versus WB audio was tested in auditory-only and audiovisual presentation modes and in quiet and noisy environments. With WB audio in a quiet environment, individuals with hearing loss exhibited better speech recognition, expended less perceived mental effort, and rated speech quality higher than with NB audio. WB audio provided a greater benefit when listening alone than when the visual channel also was available. The noisy environment significantly degraded performance for both presentation modes, but particularly for listening alone. Bit rate affected speech recognition for NB audio, and speech quality ratings for both NB and WB audio. Packet loss affected all of speech recognition, mental effort, and speech quality ratings. WB versus NB audio also affected hearing individuals, especially under packet loss. These results are discussed in terms of the practical steps they suggest for the implementation of telecommunications systems and related technical standards and policy considerations to improve the accessibility of voice telephony for people with hearing loss.