Adaptive blind separation of convolutive mixtures of independent linear signals

This paper is concerned with the problem of blind separation of independent signals (sources) from their linear convolutive mixtures. The various signals are assumed to be linear non-Gaussian but not necessarily i.i.d. An iterative, normalized higher-order cumulant maximization based approach was developed previously using the fourth-order normalized cumulants of the: “beamformed” data. A byproduct of this approach is a decomposition of the given data, at each sensor into its independent signal components. In this paper an adaptive implementation of the above approach is developed using a stochastic gradient approach. Some further enhancements including a Wiener filter implementation for signal separation and adaptive filter reinitialization are also provided. A computer simulation example is presented