We present a new approach to the cocktail party problem that uses a cortronic artificial neural network architecture (Hecht-Nielsen, 1998) as the front end of a speech processing system. Our approach is novel in three important respects. First, our method assumes and exploits detailed knowledge of the signals we wish to attend to in the cocktail party environment. Second, our goal is to provide preprocessing in advance of a pattern recognition system rather than to separate one or more of the mixed sources explicitly. Third, the neural network model we employ is more biologically feasible than are most other approaches to the cocktail party problem. Although the focus here is on the cocktail party problem, the method presented in this study can be applied to other areas of information processing.

Many feedforward neural network architectures have the property that their overall input-output function is unchanged by certain weight permutations and sign flips. In this paper, the geometric structure of these equioutput weight space transformations is explored for the case of multilayer perceptron networks with tanh activation functions (similar results hold for many other types of neural networks). It is shown that these transformations form an algebraic group isomorphic to a direct product of Weyl groups. Results concerning the root spaces of the Lie algebras associated with these Weyl groups are then used to derive sets of simple equations for minimal sufficient search sets in weight space. These sets, which take the geometric forms of a wedge and a cone, occupy only a minute fraction of the volume of weight space. A separate analysis shows that large numbers of copies of a network performance function optimum weight vector are created by the action of the equioutput transformation group and that these copies all lie on the same sphere. Some implications of these results for learning are discussed.