Independent Component Analysis based on Nonparametric Density Estimation on Time-Frequency Domain

This paper presents a novel time-frequency (TF) domain nonparametric density estimation independent component analysis (ICA) combined with preprocessing by time-frequency plane Wiener (TFPW) filtering algorithm. It achieves blind separation of over-determined instantaneous linear mixtures of non-stationary sources. The algorithm simultaneously estimates the demixing matrix and the unknown probability density functions of the source signals in TF domain. The proposed method does not require the selection of TF points or TF plane´s partition, as the latter is more restrictive to real signals. The TFPW preprocessing improves the algorithm separating effect in noisy data. As simulation shows, it works better than some TF blind separation algorithms