Advances in Blind Source Separation

Blind source separation (BSS) and related topics such as independent component analysis (ICA), sparse component analysis (SCA), or nonnegative matrix factorization (NMF) have become emerging tools inmultivariate signal processing and data analysis and are now one of the hottest and emerging areas in signal processing with solid theoretical foundations and many potential applications. In fact, BSS has become a quite important topic of research and development in many areas, especially speech enhancement, biomedical engineering, medical imaging, communication, remote sensing systems, exploration seismology, geophysics, econometrics, data mining, and so forth. The blind source separation techniques principally do not use any training data and do not assume a priori knowledge about parameters of mixing convolutive and filtering systems. Researchers from various fields are interested in different, usually very diverse aspects of BSS. BSS continues to generate a flurry of research interest, resulting in increasing numbers of papers submitted to conferences and journals. Furthermore, there are many workshops and special sessions conducted in major conferences that focus on recent research results. The International Conference on ICA and BSS is a prime example of the attractiveness and research diversity of this field. The goal of this special issue is to present the latest research in BSS/ICA.We receivedmore than 25 papers of which 10 were accepted for publication. The topics covered in this issue cover a wide range of research areas including BSS theories and algorithms, sparse representations, nonlinear mixing, and some BSS applications.