Nonlinear static and dynamic blind source separation using ensemble learning

Blind separation of sources from their nonlinear mixtures is generally a very difficult problem. This is because both the nonlinear mapping and the underlying sources are unknown, and must be learned in a completely unsupervised manner from the data. We use multilayer perceptrons as nonlinear generative models for the data, and apply Bayesian ensemble learning for finding the sources. In this paper, we first consider a static nonlinear mixture model, with a successful application to real-world speech data. Then we briefly discuss extraction of sources from nonlinear dynamic processes. In a difficult test problem with chaotic data, our approach clearly outperforms currently available nonlinear prediction techniques. The proposed methods are computationally demanding especially in the dynamic case, but they allow the use of higher dimensional nonlinear latent variable models than other existing approaches.