Abstract
This chapter focuses on the application of the harmony search algorithms to the problem of selecting more appropriate features for sound classification in digital hearing aids. Implementing sound classification algorithms embedded in hearing aids is a very challenging task. Hearing aids have to work at very low clock frequency in order to minimize power consumption, and thus maximize battery life. This necessitates the reduction of computational load while maintaining a low error probability. Since the feature extraction process is one of the most time-consuming tasks, selecting a reduced number of appropriate features is essential, thus requiring low computational cost without degrading the operation. The music-inspired harmony-search (HS) algorithm allows for effectively searching adequate solutions to this strongly constrained problem. By starting with an initial set of 74 different sound-describing features, a number of experiments were carried out to test the performance of the proposed method. Results of the harmony search algorithm are compared to those reached by other widely used methods.
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Alexandre, E., Cuadra, L., Gil-Pita, R. (2009). Sound Classification in Hearing Aids by the Harmony Search Algorithm. In: Geem, Z.W. (eds) Music-Inspired Harmony Search Algorithm. Studies in Computational Intelligence, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00185-7_11
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DOI: https://doi.org/10.1007/978-3-642-00185-7_11
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