Electroencephalograms (EEGs) are becoming increasingly important measurements of
brain activity and they have great potential for the diagnosis and treatment of mental 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 …

The problem of approximate joint diagonalization of a set of matrices is instrumental in
numerous statistical signal processing applications. For nonorthogonal joint diagonalization …

Blind source separation (BSS) methods have received extensive attention over the past two
decades; thanks to its wide applicability in a number of areas such as biomedical …

A novel multimodal approach is proposed to solve the problem of blind source separation
(BSS) of moving sources. The challenge of BSS for moving sources is that the mixing filters …

Several blind source separation algorithms obtain a separating matrix by computing the
congruence transformation that" best" diagonalizes a collection of covariance matrices …

Existing speech source separation approaches overwhelmingly rely on acoustic pressure
information acquired by using a microphone array. Little attention has been devoted to the …

A novel multimodal source separation approach is proposed for physically moving and
stationary sources which exploits a circular microphone array, multiple video cameras …

A block-based approach coupled with adaptive dictionary learning is presented for
underdetermined blind speech separation. The proposed algorithm, derived as a multi-stage …

Abstract Frequency-Domain Blind Source Separation (FD-BSS) is an efficient way to
analyze convolutive mixed speech. To improve the quality of the separated speech, a …