A well-known fact in blind deconvolution is that if the unknown source signal is white (temporally) and the unknown channel filter is minimum phase, it is possible to determine the inverse filter (equalizer) by evaluating simply the power spectral density (PSD) of the received signal. For blind source separation, however, a similar special case, equivalent to the situation in blind deconvolutio...

The multichannel blind deconvolution of finite-impulse response (FIR) or infinite-impulse response (IIR) systems is investigated using the multichannel super-exponential deflation methods. In the conventional multichannel super-exponential deflation method [4], the socalled “second-order correlation method” is incorporated in order to estimate the contributions of an extracted source signal to ...

| In this paper we present a semipara-metric approach to blind separation of nonlinear dy-namical systems with linear output equations. First we formulate blind deconvolution in a framework of semiparametric model and derive a family of estimating functions for the blind separation problem by using a nonholonomic reparametrization. The natural gradient learning algorithm is derived in the semip...

In this paper, we present frequency-domain implementations of two adaptive multichannel blind deconvolution filters that employ the independent component analysis principle. The proposed implementations achieve considerable computational gains, which is shown by performing detailed analysis on the computational complexity. Particularly, our implementations incorporate a nonholonomic constraint ...