Feature Selection for Sound Classification in Hearing Aids Through Restricted Search Driven by Genetic Algorithms

Abstract

Hearing loss may disqualify many people from leading a normal life, though the majority do not make use of hearing aids. This is because most hearing aids on the market cannot automatically adapt to the changing acoustical environment the user faces daily. This paper focuses on the development of an automatic sound classifier for digital hearing aids that aims to enhance listening comprehension when the user goes from one sound environment to another. Given the strong complexity constraints of these devices, reducing the number of signal-describing features which feed the automatic classifier is of great importance and becomes a challenging topic. Thus, the use of genetic algorithms with restricted search is explored for the mentioned feature selection. In an effort to evaluate its performance, the algorithm is compared with a standard unconstrained genetic algorithm and with sequential methods. The restricted search driven by the implemented genetic algorithm performs better than both the sequential methods and unconstrained genetic algorithms. It thus allows a subset of signal-describing features with lower cardinality to be selected. This may permit these selected features to be programmed on the digital signal processor that the hearing aid is based on, and to make efficient use of its limited computational facilities.