A scalable blind source separation paradigm aimed at sensor networks is described. The approach facilitates an unlimited number of sensors and sources and does not require a fusio...
Abstract. We present an approach for blindly decomposing an observed random vector x into f(As) where f is a diagonal function i.e. f = f1 × . . . × fm with one-dimensional funct...
We recently reported a criterion for blind separation of non-negative sources, using a new concept called convex analysis for mixtures of non-negative sources (CAMNS). Under some ...
We consider the problem of convolutive blind source separation of stereo mixtures. This is often tackled using frequency-domain independent component analysis (FDICA), or time-fre...
Maria G. Jafari, Emmanuel Vincent, Samer A. Abdall...
The variational Bayesian nonlinear blind source separation method introduced by Lappalainen and Honkela in 2000 is initialised with linear principal component analysis (PCA). Becau...
Antti Honkela, Stefan Harmeling, Leo Lundqvist, Ha...