How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?

How to cite: Saoji, A. A.; Sheikh, B. A.; Bertsch, N. J.; Goulson, K. R.; Graham, M. K.; McDonald, E. A.; Bross, A. E.; Vaisberg, J. M.; Kühnel, V.; Voss, S. C.; Qian, J.; Hogan, C. H.; DeJong, M. D. How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?. Preprints 2024, 2024110250. https://doi.org/10.20944/preprints202411.0250.v1 Saoji, A. A.; Sheikh, B. A.; Bertsch, N. J.; Goulson, K. R.; Graham, M. K.; McDonald, E. A.; Bross, A. E.; Vaisberg, J. M.; Kühnel, V.; Voss, S. C.; Qian, J.; Hogan, C. H.; DeJong, M. D. How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?. Preprints 2024, 2024110250. https://doi.org/10.20944/preprints202411.0250.v1

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MDPI and ACS Style

Saoji, A. A.; Sheikh, B. A.; Bertsch, N. J.; Goulson, K. R.; Graham, M. K.; McDonald, E. A.; Bross, A. E.; Vaisberg, J. M.; Kühnel, V.; Voss, S. C.; Qian, J.; Hogan, C. H.; DeJong, M. D. How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?. Preprints 2024, 2024110250. https://doi.org/10.20944/preprints202411.0250.v1

APA Style

Saoji, A. A., Sheikh, B. A., Bertsch, N. J., Goulson, K. R., Graham, M. K., McDonald, E. A., Bross, A. E., Vaisberg, J. M., Kühnel, V., Voss, S. C., Qian, J., Hogan, C. H., & DeJong, M. D. (2024). How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?. Preprints. https://doi.org/10.20944/preprints202411.0250.v1

Chicago/Turabian Style

Saoji, A. A., Cynthia H Hogan and Melissa D DeJong. 2024 "How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?" Preprints. https://doi.org/10.20944/preprints202411.0250.v1

Abstract

Background/Objectives Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition in their best hearing aid condition, 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) based noise reduction on cochlear implant evaluation. Methods Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratio (SNR) through one loudspeaker. Sentence recognition scores were measured for 10 (5-bilateral, 5-unilateral) 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. Results The spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. This improvement might make it difficult for some patients to achieve the

Keywords

cochlear implants; hearing aids; deep neural network

Subject

Medicine and Pharmacology, Otolaryngology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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