Closed-loop method to individualize neural-network-based audio signal processing

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 (ANF) and/or synapses (ANS) in a subject, preferably also on the integrity of inner hair cell (IHC) damage and/or outer hair cell (OHC) 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;
• 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;
• 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 an input to the personalized auditory response model or to the subject.