A Non-linear Information Maximisation Algorithm that Performs Blind Separation

A new learning algorithmis derived which performs online stochastic gradient ascent in the mutual informationbetween outputs and inputs of a network. In the absence of a priori knowledge about the `signal' and `noise' components of the input, propagation of information depends on calibrating network non-linearities to the detailed higher-order moments of the input density functions. By incidentally minimising mutual information between outputs, as well as maximising their individual entropies, the network `factorises' the input into independent components. As an example application, we have achieved near-perfect separation of ten digitally mixed speech signals. Our simulations lead us to believe that our network performs better at blind separation than the HeraultJutten network, re ecting the fact that it is derived rigorously from the mutual information objective. 

• Type: Conference
• Year: 1994
• Where: NIPS
• Authors: Anthony J. Bell, Terrence J. Sejnowski
• Keywords: Input Density Functions, Mutual Informationbetween Outputs, NIPS 1994, NIPS 2007, Stochastic Gradient Ascent