Blind source separation (BSS) and independent component analysis (ICA) are generally
based on a wide class of unsupervised learning algorithms and they found potential …

This paper describes several important methods for the blind source separation of audio
signals in an integrated manner. Two historically developed routes are featured. One started …

Providing a broad but in-depth introduction to neural network and machine learning in a
statistical framework, this book provides a single, comprehensive resource for study and …

When a parameter space has a certain underlying structure, the ordinary gradient of a
function does not represent its steepest direction, but the natural gradient does. Information …

With solid theoretical foundations and numerous potential applications, Blind Signal
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …

Blind source separation (BSS) for convolutive mixtures can be solved efficiently in the
frequency domain, where independent component analysis (ICA) is performed separately in …

Acoustic signals recorded simultaneously in a reverberant environment can be described as
sums of differently convolved sources. The task of source separation is to identify the …

Learning algorithms and underlying basic mathematical ideas are presented for the problem
of adaptive blind signal processing, especially instantaneous blind separation and …

In this chapter, we provide an overview of existing algorithms for blind source separation of
convolutive audio mixtures. We provide a taxonomy in which many of the existing algorithms …

In blind source separation, the number of sensors is usually assumed to be equal to the
number of sources. In this case, an indeterminacy appears with which any linear transform of …