This book provides a detailed survey of the methods that were recently developed 
to handle advanced versions of the blind source separation problem, which 
involve several types of nonlinear mixtures. Another attractive feature of the 
book is that it is based on a coherent framework. More precisely, the authors 
first present a general procedure for developing blind source separation 
methods. Then, all reported methods are defined with respect to this procedure. 
This allows the reader not only to more easily follow the description of each 
method but also to see how these methods relate to one another. The coherence of 
this book also results from the fact that the same notations are used throughout 
the chapters for the quantities (source signals and so on) that are used in 
various methods. Finally, among the quite varied types of processing methods 
that are presented in this book, a significant part of this description is 
dedicated to methods based on artificial neural networks, especially recurrent 
ones, which are currently of high interest to the data analysis and machine 
learning community in general, beyond the more specific signal processing and 
blind source separation communities.