Hearing-aid algorithms stop prescribing gain from the audiogram alone: closed-loop training compensates hair-cell and nerve damage separately, reaching HASPIv2 0.92 in babble
Personalized compensation for next-generation hearing aids and hearables no longer has to rely only on gain rules prescribed from the audiogram. A differentiable model of the auditory periphery is placed in a closed loop, and the difference between simulated normal-hearing and impaired-hearing responses trains a convolutional processor aimed separately at outer-hair-cell gain loss and cochlear synaptopathy. On a single fictive high-frequency sloping audiogram, the outer-hair-cell algorithm scored 0.92 on HASPIv2 in babble, against 0.46 unprocessed and 0.53 for NAL-NL2; these are model-predicted scores, not patient benefit.