ArticleScientific reports2024
Deep learning restores speech intelligibility in multi-talker interference for cochlear implant users.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A frequency analysis of filterbank initialisation and noise augmentation for LEAF.Scientific reports · 2026Article
- Multi-Level Perception Systems in Fusion of Lifeforms: Classification, Challenges and Future Conceptions.Sensors (Basel, Switzerland) · 2026Review
- Speech recognition performance with dual-microphone audio processors in mandarin-speaking cochlear implant users.Frontiers in neuroscience · 2026Article
- Leveraging spatial cues from cochlear implant microphones to efficiently enhance speech separation in naturalistic listening scenes.Scientific reports · 2025Article
- Personalizing Cochlear Implant Care in Single-Sided Deafness: A Distinct Paradigm from Bilateral Hearing Loss.Journal of personalized medicine · 2025Review
- A systematic review of machine learning approaches in cochlear implant outcomes.NPJ digital medicine · 2025Article
- Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review.Audiology research · 2025Review
- Objective Evaluation of a Deep Learning-Based Noise Reduction Algorithm for Hearing Aids Under Diverse Fitting and Listening Conditions.Trends in hearingArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Cochlear implants (CIs) do not offer the same level of effectiveness in noisy environments as in quiet settings. Current single-microphone noise reduction algorithms in hearing aids and CIs only remove predictable, stationary noise, and are ineffective against realistic, non-stationary noise such as multi-talker interference. Recent developments in deep neural network (DNN) algorithms have achieved noteworthy performance in speech enhancement and separation, especially in removing speech noise. However, more work is needed to investigate the potential of DNN algorithms in removing speech noise when tested with listeners fitted with CIs. Here, we implemented two DNN algorithms that are well suited for applications in speech audio processing: (1) recurrent neural network (RNN) and (2) SepFormer. The algorithms were trained with a customized dataset (
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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.