Improvements to the noise reduction neural network

Two experiments aimed at improving the performance of a noise reduction neural network are described. One approach is to split the last single affine transformation of the noise reduction neural network into two affine transformations and to adaptively control these transformations for noise components and speech components. The other is to further split the last single affine transformation to yield 22 affine transformations tuned for abstract concepts, or phonemes, and to adaptively control these transformations according to certain selection criteria. The latter is a refinement of the former. Both approaches are based on the fact that the speech component is more easily separated form the noise component in the second hidden layer output than in the physical input space of the network.<<ETX>>