Abstract
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.
Highlights
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
The proposed methods inherit the fast convergence properties, computational simplicity, and ease of use of the FastICA algorithm while at the same time extending this class of techniques to complex signal mixtures
Summary
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 one of the hottest and emerging areas in signal processing with solid theoretical foundations and many potential applications. Theis et al consider sparse component analysis problem for an overcomplete model using Hough transform They propose an algorithm which performs a global search for hyperplane clusters within the mixture space by gathering possible hyperplane parameters within a Hough’s accumulator tensor. By selective application of the Szego theorem which relates properties of Toeplitz and circulant matrices, normalization is derived as a special case of the generic broadband algorithm This results in a computationally efficient and fast converging algorithm without introducing typical narrowband problems such as the internal permutation problem or circularity effects. Ricardo Suyama et al proposed a method for source separation of convolutive mixture based on nonlinear prediction error filters This approach converts the original problem into an instantaneous mixture problem, which can be solved by any of the several existing methods in the literature. They employed fuzzy-filters to implement the prediction-error filter, and the efficacy of the proposed method is illustrated by some examples
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