Wavelet Based Independent Component Analysis for Multi-Channel Source Separation
Rym Moussaoui, Jean Rouat, Rémi Lefebvre · 2006
We consider the problem of separating instantaneous mixtures of different sound sources in multi-channel audio signals. Several methods have been developed to solve this problem. Independent component analysis (ICA) is certainly the most known method and the most used. ICA exploits the non-Gaussianity of the sources in the mixtures. In this study, we propose an improved signal separation algorithm where simultaneously we increase the non-Gaussian nature of signals and we initiate the preliminary separation. For this, the observations are transformed into an adequate representation using the wavelet packets decomposition. In this study, we consider the instantaneous mixture of two sources using two sensors. We validate our approach by using synthetic and recorded audio signals. Preliminary results show a strong improvement when compared to conventional ICA (FastICA), with specific signals.