Wavelet de-noising for Blind Source Separeation in Noisy Mixtures
Bertrand Rivet, Vincent Vigneron, Anisoara Paraschiv-Ionescu, Christian Jutten · HAL (Le Centre pour la Communication Scientifique Directe) · 2004
Blind source separation, which supposes that the sources are independent, is a well known domain in signal processing. However, in a noisy environment the estimation of the criterion is harder due to the noise. In strong noisy mixtures, we propose two new principles based on the combination of wavelet de-noising processing and blind source separation. We compare them in the cases of white/correlated Gaussian noise.