Reducing the computational cost for sound classification in hearing aids by selecting features via genetic algorithms with restricted search

This paper centers on designing a feature-selection algorithm able to provide a ldquosmallrdquo number of adequate features that assist a sound classification system for hearing aids in reducing its computational load without degrading its performance. Because of the problem complexity, we have explored the use of genetic algorithms with restricted search for the mentioned feature selection. In an effort to evaluate its performance, the algorithm has been compared to a standard unconstrained genetic algorithm and with sequential methods. The restricted search driven by the proposed algorithm performs better than both the sequential methods and unconstrained genetic algorithms. The proposed algorithm selects a feature subset composed of only 21 features, much smaller than the 76 features of the complete, original set of available features. This low-cardinality subset of signal-describing features is the one implemented on the hearing aid, saving thus a great number of the scarce computational resources, and making possible to put into practice the concept at reasonable cost.