AI Article Synopsis
- The study examines how Adaptive Dynamic Range Optimization (ADRO) impacts speech understanding among cochlear implant users in challenging listening environments.
- Different acoustic conditions were tested, including quiet spaces, noisy settings, and various combinations of reverberation and noise.
- Results showed that ADRO processing did not significantly enhance speech intelligibility for the participants.
Article Abstract
The aim of this study is to investigate the effect of Adaptive Dynamic Range Optimization (ADRO) on speech identification for cochlear implant (CI) users in adverse listening conditions. In this study, anechoic quiet, noisy, reverberant, noisy reverberant, and reverberant noisy conditions are evaluated. Two scenarios are considered when modeling the combined effects of reverberation and noise: (a) noise is added to the reverberant speech, and (b) noisy speech is reverberated. CI users were tested in different listening environments using IEEE sentences presented at 65 dB sound pressure level. No significant effect of ADRO processing on speech intelligibility was observed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4144166 | PMC |
| http://dx.doi.org/10.1121/1.4893334 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Continuum limit of the adaptive Kuramoto model.
Chaos
January 2025
Centre for Mathematical Science, Lund University, Märkesbacken 4, 223 62 Lund, Sweden.
We investigate the dynamics of the adaptive Kuramoto model with slow adaptation in the continuum limit, N→∞. This model is distinguished by dense multistability, where multiple states coexist for the same system parameters. The underlying cause of this multistability is that some oscillators can lock at different phases or switch between locking and drifting depending on their initial conditions.
View Article and Find Full Text PDFBenchmarking the speed-accuracy tradeoff in object recognition by humans and neural networks.
J Vis
January 2025
Department of Psychology, New York University, New York, NY, USA.
Active object recognition, fundamental to tasks like reading and driving, relies on the ability to make time-sensitive decisions. People exhibit a flexible tradeoff between speed and accuracy, a crucial human skill. However, current computational models struggle to incorporate time.
View Article and Find Full Text PDFCancer neuroscience and glioma: clinical implications.
Acta Neurochir (Wien)
January 2025
Department of Neurosurgery, University Hospital Eppendorf, Hamburg, Germany.
In recent years, it has been increasingly recognized that tumor growth relies not only on support from the surrounding microenvironment but also on the tumors capacity to adapt to - and actively manipulate - its niche. While targeting angiogenesis and modulating the local immune environment have been explored as therapeutic approaches, these strategies have yet to yield effective treatments for brain tumors and remain under refinement. More recently, the nervous system itself has been explored as a critical environmental support for cancer, with extensive neuro-tumoral interactions observed both intracranially and in extracranial sites containing neural components.
View Article and Find Full Text PDFIntegration of Immune Cell-Target Cell Conjugate Dynamics Changes the Time Scale of Immune Control of Cancer.
Bull Math Biol
January 2025
Department of Theoretical Biology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Strasse 2, 24306, Ploen, Germany.
The human immune system can recognize, attack, and eliminate cancer cells, but cancers can escape this immune surveillance. Variants of ecological predator-prey models can capture the dynamics of such cancer control mechanisms by adaptive immune system cells. These dynamical systems describe, e.
View Article and Find Full Text PDFBasic Science and Pathogenesis.
Alzheimers Dement
December 2024
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Background: Single nucleus RNA sequencing (snRNA-seq) has revolutionized our ability to dissect transcriptional profiles in specific cell types. While nuclear sequencing enhances analysis robustness, it captures only 20-50% of the cellular transcriptional information, limiting our comprehensive understanding of the cellular transcriptional ensemble. Therefore, we propose a computational approach to extract the cellular signal from bulk transcriptomic data from brain tissue, allowing us to investigate cell type-specific transcriptomic programs underlying neurodegeneration.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!