ReviewSeminars in hearing2021
Creating Clarity in Noisy Environments by Using Deep Learning in Hearing Aids.
Review in Seminars in hearing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Closedness of Acoustic Coupling and Audiological Measures Are Associated with Individual Speech-in-Noise Benefit From Noise Reduction in Hearing Aids.Trends in hearingTrial
- Characteristics of Older Adults Seeking Hearing Aids for the First Time and Initial Fitting Parameters in Mainland China.Audiology research · 2026Article
- A brain-inspired algorithm improves "cocktail party" listening for individuals with hearing loss.Communications engineering · 2025Article
- Article
- Article
- How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?Audiology research · 2024Article
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- The perception of emotion in music by people with hearing loss and people with cochlear implants.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Changing Knowledge, Principles, and Technology in Contemporary Clinical Audiological Practice: A Narrative Review.Journal of clinical medicine · 2024Review
- Digital Therapeutics in Hearing Healthcare: Evidence-Based Review.Journal of audiology & otology · 2024Article
- Novel bias-reduced coherence measure for EEG-based speech tracking in listeners with hearing impairment.Frontiers in neuroscience · 2024Article
- The Hearing Test App for Android Devices: Distinctive Features of Pure-Tone Audiometry Performed on Mobile Devices [Letter].Medical devices (Auckland, N.Z.) · 2024Article
- Cortical encoding of phonetic onsets of both attended and ignored speech in hearing impaired individuals.PloS one · 2024Article
- Restoring speech intelligibility for hearing aid users with deep learning.Scientific reports · 2023Article
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- Noise Management Preferences During Long-Term Hearing Aid Usage and Their Relation to Audiologic Factors.Trends in hearingArticle
- Speech recognition with a hearing-aid processing scheme combining beamforming with mask-informed speech enhancement.Trends in hearingArticle
- Influence of Noise Reduction on Ocular Markers of Listening Effort in Hearing Aid Users in Darkness and Ambient Light.Trends in hearingArticle
- Objective Evaluation of a Deep Learning-Based Noise Reduction Algorithm for Hearing Aids Under Diverse Fitting and Listening Conditions.Trends in hearingArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hearing aids continue to acquire increasingly sophisticated sound-processing features beyond basic amplification. On the one hand, these have the potential to add user benefit and allow for personalization. On the other hand, if such features are to benefit according to their potential, they require clinicians to be acquainted with both the underlying technologies and the specific fitting handles made available by the individual hearing aid manufacturers. Ensuring benefit from hearing aids in typical daily listening environments requires that the hearing aids handle sounds that interfere with communication, generically referred to as "noise." With this aim, considerable efforts from both academia and industry have led to increasingly advanced algorithms that handle noise, typically using the principles of directional processing and postfiltering. This article provides an overview of the techniques used for noise reduction in modern hearing aids. First, classical techniques are covered as they are used in modern hearing aids. The discussion then shifts to how deep learning, a subfield of artificial intelligence, provides a radically different way of solving the noise problem. Finally, the results of several experiments are used to showcase the benefits of recent algorithmic advances in terms of signal-to-noise ratio, speech intelligibility, selective attention, and listening effort.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.