  Sound processing in the human auditory system is complex and highly
non-linear, whereas hearing aids (HAs) still rely on simplified descriptions of
auditory processing or hearing loss to restore hearing. Even though standard HA
amplification strategies succeed in restoring audibility of faint sounds, they
still fall short of providing targeted treatments for complex sensorineural
deficits and adverse listening conditions. These shortcomings of current HA
devices demonstrate the need for advanced hearing-loss compensation strategies
that can effectively leverage the non-linear character of the auditory system.
Here, we propose a differentiable deep-neural-network (DNN) framework that can
be used to train DNN-based HA models based on biophysical auditory-processing
differences between normal-hearing and hearing-impaired systems. We investigate
different loss functions to accurately compensate for impairments that include
outer-hair-cell (OHC) loss and cochlear synaptopathy (CS), and evaluate the
benefits of our trained DNN-based HA models for speech processing in quiet and
in noise. Our results show that auditory-processing enhancement was possible
for all considered hearing-loss cases, with OHC loss proving easier to
compensate than CS. Several objective metrics were considered to estimate the
expected speech intelligibility after processing, and these simulations hold
promise in yielding improved understanding of speech-in-noise for
hearing-impaired listeners who use our DNN-based HA processing. Since our
framework can be tuned to the hearing-loss profiles of individual listeners, we
enter an era where truly individualised and DNN-based hearing-restoration
strategies can be developed and be tested experimentally.
