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Profile-analysis experiments measure the ability to discriminate complex sounds based on patterns, or profiles, in their amplitude spectra. Studies of profile analysis have focused on normal-hearing listeners and target frequencies near 1 kHz. To provide more insight into underlying mechanisms, we studied profile analysis over a large target frequency range (0.

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

Trends Hear

November 2024

NTT Communication Science Laboratories, NTT Corporation, Kanagawa, Japan.

Article Synopsis
  • - Cochlear implant (CI) users often struggle with distinguishing pitch, despite some having decent speech comprehension; this issue is linked to the quality of sound signals processed through the implants.
  • - Research shows that user sensitivity to pitch varies individually, suggesting factors like neuroplasticity or neural health impact their ability, rather than limitations in the CI signal itself.
  • - A study using deep neural networks to decode pitch from simulated CI signals found that better sound representation (more electrode channels and higher pulse rates) improves pitch estimation, especially in quiet environments, although performance drops significantly in noisy settings.
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Objective: To review the management of meningitis, large lateral skull base defect, and meningoencephalocele following pediatric cochlear implant (CI) surgery.

Study Design: Case report.

Methods: Patient demographics, medical and surgical history, computed tomography (CT).

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The objective of this research was to test whether efficient tinnitus suppression could be achieved by electrical stimulation of the single most basal electrode contact of a cochlear implant. This approach simulates the effects of electrical stimulation using a round-window electrode. The study was performed in 10 adult cochlear implant patients showing complete or almost complete tinnitus suppression during electrical stimulation with their standard fitting-MAP.

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Cochlear implants (CI) allow deaf patients to improve language perception and improving their emotional valence assessment. Electroencephalographic (EEG) measures were employed so far to improve CI programming reliability and to evaluate listening effort in auditory tasks, which are particularly useful in conditions when subjective evaluations are scarcely appliable or reliable. Unfortunately, the presence of CI on the scalp introduces an electrical artifact coupled to EEG signals that masks physiological features recorded by electrodes close to the site of implant.

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