This paper explores the application of the music-inspired Harmony-Search algorithm to the problem of feature selection for sound classification in digital hearing aids. The importance of this problem is given by the strong computational constraints inherent to the DSPs used in modern digital hearing aids. The goal of the feature selection algorithm is to select a subset of features in order to reduce the computational complexity of the system while maintaining a low probability of error. A set of experiments will be performed to test the performance of the proposed system, using a total of 74 different features. The results will be compared with those obtained using other widely-used algorithms, such as sequential search algorithms or random search.
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