Improving learning efficiency in multi-objective simulated annealing programming for sound environment classification

In this work, a classifier that jointly optimises the expected total classification cost and the energy consumption is presented. A numerical study is provided, where different alternatives are implemented on a hearing aid. Our proposal is capable of automatically classifying the acoustic environment that surrounds the user and choosing the parameters of the amplification that are best adapted to the user's comfort, while attaining relevant improvements in both classification and learning-related energy consumptions with small to negligible loss in classification accuracy.

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