{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T19:20:56Z","timestamp":1773429656365,"version":"3.50.1"},"reference-count":69,"publisher":"Acoustical Society of America (ASA)","issue":"3","funder":[{"name":"Horizon 2020 Research and Innovation Programme","award":["678120"],"award-info":[{"award-number":["678120"]}]},{"name":"FWO Machine Hearing 2.0","award":["216318G"],"award-info":[{"award-number":["216318G"]}]},{"name":"EIC transition grant EarDiTech","award":["101058278"],"award-info":[{"award-number":["101058278"]}]}],"content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:p>Conventional hearing aid (HA) algorithms, developed based on computational and non-differentiable auditory processing models, do not typically compensate for cochlear synaptopathy (CS). Traditional HAs apply fixed or rule-based gain adjustments within predefined frequency bands and compression ratios, instead of model-based fitting that numerically optimizes processing from neural representations of normal and impaired hearing. To compensate for combined CS and outer-hair-cell (OHC) loss, deep neural network (DNN)-based closed-loop HA systems are gaining traction. Here, we present several DNN-based HA algorithms that embed personalized, differentiable DNN-based auditory models (dCoNNear) inside a closed-loop system to train personalized HA algorithms compensating for OHC damage and\/or CS. The HA algorithms were trained using backpropagation to minimize differences between hearing-impaired and normal-hearing auditory nerve (AN) responses. Performance was evaluated using speech and standard auditory stimuli. Results showed enhanced temporal-envelope (TENV) processing of modulated pure tones, particularly for CS, where sharpening of the TENV led to stronger AN onset responses. Transfer functions indicated that DNN-based HA algorithms applied adaptive level-dependent and frequency-specific gain aligned with OHC damage. The algorithms improved the normalized root mean square error of AN responses compared to NAL-NL2 for certain TIMIT phoneme categories. This evaluation offers insights into how machine-learning approaches outperform traditional HA strategies.<\/jats:p>","DOI":"10.1121\/10.0042995","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T18:20:27Z","timestamp":1773426027000},"page":"2315-2339","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of deep neural network-based strategies for the compensation of sensorineural hearing loss"],"prefix":"10.1121","volume":"159","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6872-9461","authenticated-orcid":false,"given":"Marjoleen","family":"Wouters","sequence":"first","affiliation":[{"name":"Department of Information Technology, Hearing Technology at Wireless, Acoustics, Environment and Expert Systems 1 , Ghent,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8594-7314","authenticated-orcid":false,"given":"Chuan","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Hearing Technology at Wireless, Acoustics, Environment and Expert Systems 1 , Ghent,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Attila","family":"Fr\u00e1ter","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Hearing Technology at Wireless, Acoustics, Environment and Expert Systems 1 , Ghent,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1817-5370","authenticated-orcid":false,"given":"Guy","family":"Torfs","sequence":"additional","affiliation":[{"name":"Department of Information Technology, IDLAB 2 , Ghent,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6498-7719","authenticated-orcid":false,"given":"Sarah","family":"Verhulst","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Hearing Technology at Wireless, Acoustics, Environment and Expert Systems 1 , Ghent,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"231","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"issue":"4","key":"2026031314202072500_c1","doi-asserted-by":"publisher","first-page":"EL302","DOI":"10.1121\/1.4896416","article-title":"Transmission line cochlear models: Improved accuracy and efficiency","volume":"136","year":"2014","journal-title":"J. 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(2023). \u201cNeural network model for cochlear mechanics and processing,\u201d U.S. patent 11,800,301."},{"key":"2026031314202072500_c56","doi-asserted-by":"publisher","first-page":"3842","DOI":"10.1121\/1.4763989","article-title":"Nonlinear time-domain cochlear model for transient stimulation and human otoacoustic emission","volume":"132","year":"2012","journal-title":"J. Acoust. Soc. Am."},{"key":"2026031314202072500_c57","unstructured":"Verhulst, S., Drakopoulos, F., Van Den Broucke, A., and Keshishzadeh, S. (2025). \u201cClosed-loop method to individualize neural-network-based audio signal processing,\u201d U.S. patent 12,212,929."},{"key":"2026031314202072500_c58","doi-asserted-by":"publisher","first-page":"2331216516672186","DOI":"10.1177\/2331216516672186","article-title":"Individual differences in auditory brainstem response wave characteristics","volume":"20","year":"2016","journal-title":"Trends Hear."},{"key":"2026031314202072500_c59","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.heares.2015.04.014","article-title":"Cochlear neuropathy in human presbycusis: Confocal analysis of hidden hearing loss in post-mortem tissue","volume":"327","year":"2015","journal-title":"Hear. 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