Joint design of Gaussianized spectrum-based features and least-square linear classifier for automatic acoustic environment classification in hearing aids

In this paper we propose a method to generate a novel set of features in order to improve sound classification in digital hearing aids. The approach is based on the fact that those classification algorithms whose design consists in minimizing the mean squared error work better when the data to be classified exhibit a Gaussian distribution. The novel features we propose are thus based on sound spectral magnitudes that, prior   to the feature calculation itself, are Gaussianized by a power law parametrized by a design parameter, αα. The explored method allows to jointly   design the sound features and a least-square linear classifier, whose design parameters are also parametrized by αα. The experimental work suggests that there is a proper value of αα for which the so-designed classifier, fed with the novel features, exhibits a low error probability. Moreover, we have found that the method can be extended to nonlinear classifiers also trained by minimizing the mean squared error, such as, for instance, neural networks.