Recent Advances in Hearing Aid Technology: From Signal Processing to Artificial Intelligence
Main Article Content
http://orcid.org/0009-0003-6469-8567
Ahmet Arslanoğlu, MD
http://orcid.org/0000-0002-2856-716X
Abstract
Communication, social engagement, and overall quality of life are profoundly affected by hearing loss, one of the most common sensory impairments globally. Rapid technological advances in hearing aids over the past 20 years have transformed devices from basic analog amplification systems to more complex digital ones that use artificial intelligence and machine learning. This review's thorough examination focuses on innovations in signal processing techniques, adaptive features, and the incorporation of artificial intelligence for customized sound amplification and adaptive environments. This paper will focus on the latest developments in directional microphone arrays, noise reduction methods, and feedback cancellation, as well as the rise of deep learning as a tool to improve these features across different types of hearing loss.
Our goal in this study is to provide readers with a bird's-eye view of the latest developments in hearing-aid technology, with an emphasis on digital signal processing, background-noise reduction, wireless connectivity, customization, and artificial-intelligence-powered adaptive systems. We discuss new developments, including sound classification using deep learning, real-time adaptation to surroundings, and integration with wearable ecosystems. The next sections will explain the functional improvements in modern hearing aids by carefully examining the computational frameworks that underpin them and how they affect auditory perception. This analysis will focus on research into hearing aids, including power consumption, miniaturization, and the integration of biological sensors to improve health monitoring and the user experience. This article aims to outline the current state of the art and forecast future developments in audiological prosthetics by critically examining these technological paths.
To help academics and clinicians understand the complex relationship between engineering and audiology that underpins contemporary hearing rehabilitation, a comprehensive review such as this one is essential. Optimizing patient outcomes and enabling the development of next-generation devices that address unmet needs in complex listening contexts depend on understanding these technological intricacies.
In addition, the article examines how these technologies have affected clinical outcomes, patient satisfaction, and the future of auditory rehabilitation. As part of this process, we will optimize sound quality across a variety of acoustic scenarios and investigate how these innovations enhance speech intelligibility in complex listening environments.
Topics covered include digital signal processing, artificial intelligence, noise reduction, auditory rehabilitation, and hearing aids.
Article Details
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
2. Einhorn R. Hearing Aid Technology for the 21st Century: A Proposal for Universal Wireless Connectivity and Improved Sound Quality. IEEE Pulse. 2017;8(2):25-28. doi:10.1109/mpul.2016.2647018
3. Beck DL. Hearing, listening and deep neural networks in hearing aids. Journal of Otolaryngology-ENT Research. 2021;13(1):5-8. doi:10.15406/joentr.2021.13.00481
4. Ramachandra B, Nalina HD. A Survey of Recent Advances in Hearing Aid Technologies and Trends. International Research Journal on Advanced Engineering Hub (IRJAEH). 2024;2(2):303-308. doi:10.47392/irjaeh.2024.0046
5. Kim S, Lee HM. Current Status and Future Trends in Hearing Aid Technology. Journal of Clinical Otolaryngology Head and Neck Surgery. 2023;34(4):111-118. doi:10.35420/jcohns.2023.34.4.111
6. Zhang T, Mustiere F, Micheyl C. Intelligent hearing aids: The next revolution. 2016;2016:72-76. doi:10.1109/embc.2016.7590643
7. Vries de BB. An Integrated Approach to Hearing Aid Algorithm Design. TU/e Research Portal. Published online January 1, 2008. Accessed November 2025. https://research.tue.nl/en/publications/c8c6a975-cd72-41ed-91b5-fad02c735128
8. Brammer AJ, Pan GJ. Active control of sounds with large dynamic range. The Journal of the Acoustical Society of America. 2000;108:2464-2464. doi:10.1121/1.4743082
9. Blamey PJ, Macfarlane DS, Steele BR. An Intrinsically Digital Amplification Scheme for Hearing Aids. EURASIP Journal on Advances in Signal Processing. 2005;2005(18). doi:10.1155/asp.2005.3026
10. Chen X, Yu X, Chang L, et al. A Synergistic Framework of Nonlinear Acoustic Computing and Reinforcement Learning for Real-World Human-Robot Interaction. arXiv (Cornell University). Published online May 4, 2025. doi:10.48550/arxiv.2505.01998
11. Kerckhoff J, Listenberger J, Valente M. Advances in Hearing Aid Technology. Contemporary Issues in Communication Science and Disorders. 2008;35:102-112. doi:10.1044/cicsd_35_f_102
12. Luo H, Arndt H. Advanced signal processing technologies for intelligent hearing aids. Canadian acoustics. 2005;33(3):64-65. Accessed October 2025. https://jcaa.caa-aca.ca/index.php/jcaa/article/view/1746
13. Bentler RA, Tubbs JL, Egge JLM, Flamme GA, Dittberner A. Evaluation of an Adaptive Directional System in a DSP Hearing Aid. American Journal of Audiology. 2004;13(1):73-79. doi:10.1044/1059-0889(2004/010)
14. Karadogan SG. Towards Cognizant Hearing Aids: Modeling of Content, Affect and Attention. Research Portal Denmark. 2012;(275):142. Accessed July 2025. https://local.forskningsportal.dk/local/dki-cgi/ws/cris-link?src=dtu&id=dtu-f06079c4-23c5-4590-9a78-0a950d0c1286&ti=Towards%20Cognizant%20Hearing%20Aids%3A%20Modeling%20of%20Content%2C%20Affect%20and%20Attention
15. Yund EW, Buckles KM. Enhanced speech perception at low signal-to-noise ratios with multichannel compression hearing aids. The Journal of the Acoustical Society of America. 1995;97(2):1224-1240. doi:10.1121/1.412232
16. Yund EW, Hj S, Efron R. Speech discrimination with an 8-channel compression hearing aid and conventional aids in background of speech-band noise. PubMed. 1987;24(4):161-180. Accessed November 2025. https://pubmed.ncbi.nlm.nih.gov/3430375
17. Bustamante DK, Braida LD. Principal-component amplitude compression for the hearing impaired. The Journal of the Acoustical Society of America. 1987;82(4):1227-1242. doi:10.1121/1.395259
18. Overby N. Scene-aware compensation strategies for hearing aids in adverse conditions. Research Portal Denmark. 2023;(59):131. Accessed August 2025. https://local.forskningsportal.dk/local/dki-cgi/ws/cris-link?src=dtu&id=dtu-a353e83d-b5d1-4f96-bd2c-04605e5c054f&ti=Scene-aware%20compensation%20strategies%20for%20hearing%20aids%20in%20adverse%20conditions
19. Moore BCJ. Hearing Aids: What Works Well and What Can Be Improved. Journal of the Association for Research in Otolaryngology. Published online February 13, 2026. doi:10.1007/s10162-026-01031-5
20. Wang R, Harjani R. Acoustic feedback cancellation in hearing aids. In: IEEE International Conference on Acoustics Speech and Signal Processing. ; 1993:137. doi:10.1109/icassp.1993.319074
21. Healy EW, Delfarah M, Johnson EM, Wang D. A deep learning algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker and reverberation. The Journal of the Acoustical Society of America. 2019;145(3):1378-1388. doi:10.1121/1.5093547
22. Schweitzer C, Krishnan G. Binaural beamforming and related digital processing for enhancement of signal-to-noise ratio in hearing aids. Current Opinion in Otolaryngology & Head & Neck Surgery. 1996;4(5):335-339. doi:10.1097/00020840-199610000-00008
23. Andersen AH, Santurette S, Pedersen MS, et al. Creating Clarity in Noisy Environments by Using Deep Learning in Hearing Aids. Seminars in Hearing. 2021;42(3):260-281. doi:10.1055/s-0041-1735134
24. Clark JL, Swanepoel DW. Technology for hearing loss – as We Know it, and as We Dream it. Disability and Rehabilitation Assistive Technology. 2014;9(5):408-413. doi:10.3109/17483107.2014.905642
25. Edwards B. Emerging Technologies, Market Segments, and MarkeTrak 10 Insights in Hearing Health Technology. Seminars in Hearing. 2020;41(1):37-54. doi:10.1055/s-0040-1701244
26. Rathna R, Anu VM, Mishra S. Real-time Smart Auditory Assistive Wearable (RESAAW) for People with Different Degrees of Hearing Loss. Revue d intelligence artificielle. 2024;38(4):1319-1325. doi:10.18280/ria.380425
27. Johansen B. Data Driven User Experience for Personalizing Hearing Health Care. Research Portal Denmark. Published online January 1, 2019:215. Accessed August 2025. https://local.forskningsportal.dk/local/dki-cgi/ws/cris-link?src=dtu&id=dtu-3d7d1a23-5c43-4252-b95e-3061747df622&ti=Data%20Driven%20User%20Experience%20for%20Personalizing%20Hearing%20Health%20Care
28. Johansen B, Petersen MK, Pontoppidan NH, Sandholm P, Larsen JE. Rethinking Hearing Aid Fitting by Learning From Behavioral Patterns. Published online May 1, 2017:1733-1739. doi:10.1145/3027063.3053156
29. Mascolo C. Sounding out the Power of Audio for Health. IEEE Pervasive Computing. 2024;23(3):67-69. doi:10.1109/mprv.2024.3429779
30. Roberts B, Kardous CA, Neitzel RL. Improving the accuracy of smart devices to measure noise exposure. Journal of Occupational and Environmental Hygiene. 2016;13(11):840-846. doi:10.1080/15459624.2016.1183014
31. Alishbayli A, Schlegel NJ, Englitz B. Using auditory texture statistics for domain-neutral removal of background sounds. Frontiers in Audiology and Otology. 2023;1. doi:10.3389/fauot.2023.1226946
32. Pasta A, Szatmari TI, Christensen JH, et al. Clustering Users Based on Hearing Aid Use: An Exploratory Analysis of Real-World Data. Frontiers in Digital Health. 2021;3. doi:10.3389/fdgth.2021.725130
33. Meng Q, Chen J, Zhang C, Wasmann JWA, Barbour DL, Zeng F. Editorial: Digital hearing healthcare. Frontiers in Digital Health. 2022;4. doi:10.3389/fdgth.2022.959761
34. Balling LW, Mølgaard LL, Townend O, Nielsen JB. The Collaboration between Hearing Aid Users and Artificial Intelligence to Optimize Sound. Seminars in Hearing. 2021;42(3):282-294. doi:10.1055/s-0041-1735135
35. Mu Z, Yang X, Wang G. SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation. arXiv (Cornell University). Published online May 6, 2025. doi:10.48550/arxiv.2505.03273
36. Ibrahemm Z h., Shihab AI. Voice separation and recognition using machine learning and deep learning a review paper. Journal of Al-Qadisiyah for Computer Science and Mathematics. 2023;15(3). doi:10.29304/jqcm.2023.15.3.1262
37. Modupe OT, Otitoola AA, Oladapo OJ, et al. REVIEWING THE TRANSFORMATIONAL IMPACT OF EDGE COMPUTING ON REAL-TIME DATA PROCESSING AND ANALYTICS. Computer Science & IT Research Journal. 2024;5(3):693-702. doi:10.51594/csitrj.v5i3.929
38. Nada N, Moaty AS. Beyond amplification: how artificial intelligence is redefining hearing. The Egyptian Journal of Otolaryngology. 2025;41(1). doi:10.1186/s43163-025-00965-6
39. Cox RM, Johnson J, Xu J. Impact of Hearing Aid Technology on Outcomes in Daily Life I: The Patients’ Perspective. Ear and Hearing. 2016;37(4). doi:10.1097/aud.0000000000000277
40. Nielsen J, Nielsen J, Larsen J. Perception-based Personalization of Hearing Aids using Gaussian Processes and Active Learning. IEEE/ACM Transactions on Audio Speech and Language Processing. Published online January 1, 2014:1-1. doi:10.1109/taslp.2014.2377581
41. Yang T, Chen YF, Cheng Y, Huang JN, Wu C, Chu YC. Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S. BioData Mining. 2023;16(1). doi:10.1186/s13040-023-00351-z
42. Doherty KA, Desjardins JL. The benefit of amplification on auditory working memory function in middle-aged and young-older hearing impaired adults. Frontiers in Psychology. 2015;6. doi:10.3389/fpsyg.2015.00721
43. Dillon MT, Buss E, Adunka MC, et al. Long-term Speech Perception in Elderly Cochlear Implant Users. JAMA Otolaryngology–Head & Neck Surgery. 2013;139(3):279-279. doi:10.1001/jamaoto.2013.1814
44. Perron M. Hearing Aids of Tomorrow: Cognitive Control Toward Individualized Experience. The Hearing Journal. 2017;70(11). doi:10.1097/01.hj.0000527206.21194.fa
45. Szatmari TI. Personalizing Audiology With User-Centered, Private AI. Research Portal Denmark. Published online January 1, 2024:186. Accessed July 2025. https://local.forskningsportal.dk/local/dki-cgi/ws/cris-link?src=dtu&id=dtu-9f5b4368-5d13-4897-897f-76feacdfbace&ti=Personalizing%20Audiology%20With%20User-Centered%2C%20Private%20AI