ArticleAudiology research2024
How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?
Article in Audiology research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Continuous vs. Interrupted Multi-Talker Babble Background Noise: Impact on Speech Perception in Bimodal Cochlear Implants.Journal of clinical medicine · 2026Article
- Brain benefits of deep learning-based noise management in experienced hearing aid users using functional near infrared spectroscopy.Scientific reports · 2025Article
- DNN-Based Noise Reduction Significantly Improves Bimodal Benefit in Background Noise for Cochlear Implant Users.Journal of clinical medicine · 2025Article
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Authors and funding
13 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesAdult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition under the best hearing aid conditions, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) noise reduction on cochlear implant evaluation.
methodsSpeech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratios (SNRs) through one loudspeaker. Sentence recognition scores were measured for 10 hearing-impaired patients using three hearing aid programs: calm situation, speech in noise, and spheric speech in loud noise (DNN-based noise reduction). Speech perception results were compared to bench analyses comprising the phase inversion technique, employed to predict SNR improvement, and the Hearing-Aid Speech Perception Index (HASPI v2), utilized to predict speech intelligibility.
resultsThe spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. Thus, DNN-based noise reduction can improve speech perception in noisy environments, potentially reducing the need for cochlear implants in some cases. The phase inversion method showed a 4-5 dB SNR improvement for the DNN-based noise reduction program compared to the other two programs. HASPI v2 predicted slightly better speech intelligibility than was measured in this study.
conclusionsDNN-based noise reduction might make it difficult for some patients with significant residual hearing to qualify for cochlear implantation, potentially delaying its adoption or eliminating the need for it entirely.
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