Advances in Blind Source Separation
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Editorial Advances in Blind Source Separation Andrzej Cichocki1 and Frank Ehlers2 1 Laboratory 2 NATO
for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Hirosawa 2-1, Wako-shi Saitama 351-0198, Japan Undersea Research Centre, Viale S. Bartolomeo 400, 19138 La Spezia, Italy
Received 23 August 2006; Accepted 23 August 2006 Copyright © 2007 A. Cichocki and F. Ehlers. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 in multivariate 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 received more 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. Theory and Algorithms for ICA/SCA In the first paper in this issue, Thomas Melia and Scott Rickard present DESPIRIT algorithm which is an extension of the DUET Blind Source Separation algorithm which
can demix an arbitrary number of speech signals using only two anechoic mixtures of the signals. The DUETESPRIT (DESPRIT) Blind Source Separation algorithm extends DUET to situations where sparsely echoic mixtures of an arbitrary number of sources overlap in timefrequency. This paper outlines the development of the DESPRIT method and demonstrates its properties through various experiments conducted on synthetic and real world mixtures. In the second paper Scott Douglas developed new fixedpoint algorithms for the blind separation of complex-valued mixtures of non-circularly-symmetric, and non-Gau
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