An artificial neural network-based method for converting an auditory stimulus to a processed auditory output, the method comprising the steps of: a. generating a neural network-based personalized auditory response model based at least on the integrity of auditory nerve fibers and/or auditory nerve synapses in a subject, preferably also on the integrity of inner hair cell damage and/or outer hair cell damage in said subject; said personalized auditory response model representing an expected auditory response of said subject with an auditory profile to the auditory stimulus; b. comparing the output of the personalized auditory response model with the output of a neural network-based desired auditory response model to determine an auditory response difference; wherein said neural network-based models consist of non-linear operations that make the auditory response difference differentiable such that they are capable of reiterating at least one component by optimizing along a computable gradient using an optimization algorithm; wherein a reference neural network that describes a normal-hearing auditory periphery is used as the desired auditory response model and a corresponding hearing-impaired neural network is used as the personalized auditory response model; c. using the determined differentiable auditory response difference to develop a neural network-based individualized auditory signal processing model of the subject, wherein the individualized auditory signal processing model is configured to minimize the determined auditory response difference; wherein the individualized auditory signal processing model is a signal processing neural network model trained to process the auditory input and compensate for the degraded output of the hearing-impaired model, when connected to an input of the hearing-impaired model or to the subject; and, d. applying the individualized neural-network-based auditory signal processing model to the auditory stimulus to produce a processed auditory output that matches the desired auditory response, when given as the input to the personalized auditory response model or to the subject. a. generating a neural network-based personalized auditory response model based at least on the integrity of auditory nerve fibers and/or auditory nerve synapses in a subject, preferably also on the integrity of inner hair cell damage and/or outer hair cell damage in said subject; said personalized auditory response model representing an expected auditory response of said subject with an auditory profile to the auditory stimulus; b. comparing the output of the personalized auditory response model with the output of a neural network-based desired auditory response model to determine an auditory response difference; wherein said neural network-based models consist of non-linear operations that make the auditory response difference differentiable such that they are capable of reiterating at least one component by optimizing along a computable gradient using an optimization algorithm; wherein a reference neural network that describes a normal-hearing auditory periphery is used as the desired auditory response model and a corresponding hearing-impaired neural network is used as the personalized auditory response model; c. using the determined differentiable auditory response difference to develop a neural network-based individualized auditory signal processing model of the subject, wherein the individualized auditory signal processing model is configured to minimize the determined auditory response difference; wherein the individualized auditory signal processing model is a signal processing neural network model trained to process the auditory input and compensate for the degraded output of the hearing-impaired model, when connected to an input of the hearing-impaired model or to the subject; and, d. applying the individualized neural-network-based auditory signal processing model to the auditory stimulus to produce a processed auditory output that matches the desired auditory response, when given as the input to the personalized auditory response model or to the subject.