ON-LINE MINIMUM MUTUAL INFORMATION METHOD FOR TIME-VARYING BLIND SOURCE SEPARATION

The MeRMaId (Minimum Renyi’s Mutual Information) algorithm for BSS (blind source separation) has previously been shown to outperform several popular algorithms in terms of data efficiency. The algorithms it compared favorably with include Hyvarinen’s FastICA, Bell and Sejnowski’s Infomax, and Comon’s MMI (Minimum Mutual Information) methods. The drawback is that the MeRMaId algorithm has a computational complexity of O(L2), as compared to O(L) for the other three. However, a new advancement referred to as SIG (Stochastic Information Gradient), can be used to modify the MeRMaId criterion such that the complexity is reduced to O(L). The modified criterion is then applied to the separation of instantaneously mixed sources using an on-line implementation. Simulations demonstrate that the new algorithm preserves the separation performance of the original algorithm and, in fact, compares quite favorably with several published methods.