Advances in Blind Source Separation

Andrzej Cichocki, Frank Ehlers
2007 EURASIP Journal on Advances in Signal Processing  
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
more » ... specially 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 DUET-ESPRIT (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 DE-SPRIT 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-Gaussian independent source signals. Leveraging recently-developed results on the separability of complex-value signal mixtures, he systematically constructed iterative procedures on a kurtosis-based contrast whose evolutionary characteristics are identical to those of the FastICA algorithm of Hyvarinen and Oja in the real-valued mixtures case. 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. For extracting multiple sources, symmetric and asymmetric signal deflation procedures have been employed. Simulation for both noiseless and noisy mixtures indicate that the proposed algorithms have superior finite-sample performance in data-starved scenarios as compared to existing complex ICA methods while performing about as well as the best of these techniques for larger data record lengths. In the third paper, Fabian J. 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. This renders the
doi:10.1155/2007/57314 fatcat:jha57qhoxbhlphi54epmf7qp4i