Speech signal restoration using an optimal neural network structure

In this paper, we propose an optimal neural network-based method for noisy speech restoration. The method uses a feedforward neural network with one hidden layer as a nonlinear predictive filter. In order to select the optimal network structure, we apply the predictive minimum description length principle to determine the optimal number of input and hidden nodes. In this way, the possible over-fitting and under-fitting problem can be penalized automatically. This results in a computationally efficient network structure with both excellent noise attenuation and generalization capabilities.