A family of MLP based nonlinear spectral estimators for noise reduction
Fei Xie, Dirk Van Compernolle · 2002
In this paper we present a family of nonlinear spectral estimators for noise reduction which are approximated and implemented by a multilayer perceptron neural network. The estimators are approximations of the true minimum mean square error estimator in the logarithmic or a related perceptual domain. Training data for the neural networks is generated from relevant statistical speech and noise models. One single estimator network is generated for all frequency channels. Parameters describing both the noise and speech distribution are estimated on line and provided as extra inputs to the neural net. Including these parameters significantly improves performance over standard spectral estimators which are based on a global speech model and a noise model described by a single parameter, the noise mean.>