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 …

With the advent of the era of big data information, artificial intelligence (AI) methods have
become extremely promising and attractive. It has become extremely important to extract …

Introduction to Petroleum Seismology, second edition, provides the theoretical and practical
foundation for tackling present and future challenges of petroleum seismology, especially …

Independent component analysis (ICA) of fMRI data generates session/individual specific
brain activation maps without a priori assumptions regarding the timing or pattern of the …

The organization of brain areas in functionally connected networks, their dynamic changes,
and perturbations in disease states are subject of extensive investigations. Research on …

Independent component analysis (ICA) is a widely applied technique to derive functionally
connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector …

Utilizing dependence over frequencies has shown significant excellence in tackling the
frequency-domain blind source separation (BSS), which is also referred to as independent …

To efficiently and accurately separate sources from the measured signals and align their
permutation, a convolutive blind source separation (BSS) in frequency domain with kurtosis …

A novel extension of Independent Component and Independent Vector Analysis for blind
extraction/separation of one or several sources from time-varying mixtures is proposed. The …

Speech signals observed from distantly placed microphones may have some acoustic
interference, such as noise and reverberation. These may lead to the degradation of the …