Investigating the Effect of Blurring and Focusing Current in Cochlear Implant Users with the Panoramic ECAP Method.

Purpose: For some cochlear implants (CIs), it is possible to focus electrical stimulation by partially returning current from the active electrode to nearby, intra-cochlear electrodes (partial tripolar (pTP) stimulation). Another method achieves the opposite: "blurring" by stimulating multiple electrodes simultaneously. The Panoramic ECAP (PECAP) method provides a platform to investigate their effects in detail by measuring electrically evoked compound action potentials and estimating current spread and neural responsiveness along the length of the CI electrode array.

Neural Decoding of the Speech Envelope: Effects of Intelligibility and Spectral Degradation.

During continuous speech perception, endogenous neural activity becomes time-locked to acoustic stimulus features, such as the speech amplitude envelope. This speech-brain coupling can be decoded using non-invasive brain imaging techniques, including electroencephalography (EEG). Neural decoding may provide clinical use as an objective measure of stimulus encoding by the brain-for example during cochlear implant listening, wherein the speech signal is severely spectrally degraded.

Using deep learning to improve the intelligibility of a target speaker in noisy multi-talker environments for people with normal hearing and hearing loss.

Understanding speech in noisy environments is a challenging task, especially in communication situations with several competing speakers. Despite their ongoing improvement, assistive listening devices and speech processing approaches still do not perform well enough in noisy multi-talker environments, as they may fail to restore the intelligibility of a speaker of interest among competing sound sources. In this study, a quasi-causal deep learning algorithm was developed that can extract the voice of a target speaker, as indicated by a short enrollment utterance, from a mixture of multiple concurrent speakers in background noise.

Recovering speech intelligibility with deep learning and multiple microphones in noisy-reverberant situations for people using cochlear implants.

For cochlear implant (CI) listeners, holding a conversation in noisy and reverberant environments is often challenging. Deep-learning algorithms can potentially mitigate these difficulties by enhancing speech in everyday listening environments. This study compared several deep-learning algorithms with access to one, two unilateral, or six bilateral microphones that were trained to recover speech signals by jointly removing noise and reverberation.

A new method for quantifying Cu in nuclear debris samples.

Quantifying Cu in post-detonation nuclear debris samples can provide important diagnostic information regarding the structural materials used within a nuclear device. However, this task is challenging due to the weak gamma emissions associated with the decay of Cu, its short half-life (12.701 h), and the presence of interfering fission product radioisotopes.