On the feasibility of using neural nets to derive hearing-aid prescriptive procedures.

A neural net is a "black box" information processing system that can be used for pattern matching, optimal prediction, or functional approximation. A neural net requires a minimal amount of a priori knowledge about the problem to be solved, but can require large amounts of data to converge to a solution. For a hearing-aid fitting procedure, a multilayer perceptron net was trained to generate an optimum match between a set of input pure-tone audiograms and the corresponding best frequency response and gain for each subject. The feasibility of using neural nets to select hearing-aid response characteristics was tested using both simulated and real audiometric data. The simulation results indicate that a neural net can be successfully trained to reproduce a fitting rule such as the NAL-R procedure, and that a minimum of about 50 sets of audiometric response data are needed for the net to converge to a generalized solution. When used to predict, from the pure-tone audiograms, the best frequency response characteristics determined for subjects having severe-to-profound hearing losses, the neural net was more accurate than the NAL-R fitting procedure derived from the same data.