A novel recurrent network for independent component analysis of post nonlinear convolutive mixtures
Daniele Vigliano, Raffaele Parisi, Aurelio Uncini · 2004
The paper introduces a novel independent component analysis approach to the separation of nonlinear convolutive mixtures. In particular, convolutive mixing of post nonlinear mixtures is considered. Source separation is performed by a new efficient recurrent network, which is able to ensure faster training with respect to currently available feedforward architectures, with lower computational costs. The proposed architecture makes proper use of flexible spline neurons for on-line estimation of the score function. Experimental results are described to demonstrate the effectiveness of the proposed technique.