4 research outputs found
Fetal electrocardiogram extraction by sequential source separation in the wavelet domain
This work addresses the problem of fetal electrocardiogram extraction using blind source separation (BSS) in the wavelet domain. A new approach is proposed, which is particularly advantageous when the mixing environment is noisy and time-varying, and that is shown, analytically and in simulation, to improve the convergence rate of the natural gradient algorithm. The distribution of the wavelet coefficients of the source signals is then modeled by a generalized Gaussian probability density, whereby in the time-scale domain the problem of selecting appropriate nonlinearities when separating mixtures of both sub- and super-Gaussian signals is mitigated, as shown by experimental results
A novel adaptive algorithm for the blind separation of periodic sources
An adaptive algorithm for the blind separation of
periodic sources is proposed in this paper. The method uses
only the second order statistics of the data, and exploits the
periodic nature of the source signals. Simulation results show
that the proposed approach has the ability to restore statistical
independence, and its performance is comparable to that of a
well established, higher order, blind source separation method
Normalised natural gradient algorithm for the separation of cyclostationary sources
A normalised natural gradient algorithm (NGA) for the separation of cyclostationary source signals is proposed in this paper. It improves the convergence properties of the cyclostationary natural gradient algorithm (CSNGA) by employing a gradient adaptive learning rate whose value changes in response to some change in the filter parameters. Experimental results demonstrate the improved behaviour of the approach
Blind separation of convolutive mixtures of cyclostationary sources using an extended natural gradient method
An on-line adaptive blind source separation algorithm for
the separation of convolutive mixtures of cyclostationary
source signals is proposed. The algorithm is derived by applying natural gradient iterative learning to the novel cost
function which is defined according to the wide sense cyclostationarity
of signals. The efficiency of the algorithm
is supported by simulations, which show that the proposed
algorithm has improved performance for the separation of
convolved cyclostationary signals in terms of convergence
speed and waveform similarity measurement, as compared
to the conventional natural gradient algorithm for convolutive
mixtures