By Yong Xiang, Dezhong Peng, Zuyuan Yang

ISBN-10: 9812872264

ISBN-13: 9789812872265

ISBN-10: 9812872272

ISBN-13: 9789812872272

This publication presents readers a whole and self-contained set of information approximately established resource separation, together with the most recent improvement during this box. The publication offers an summary on blind resource separation the place 3 promising blind separation concepts which may take on together correlated assets are provided. The ebook additional specializes in the non-negativity established equipment, the time-frequency research established equipment, and the pre-coding dependent tools, respectively.

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Extra resources for Blind Source Separation: Dependent Component Analysis

Example text

By assuming that at most one source signal possesses the dominant energy at each sample point, which is called as the W-disjoint orthogonal condition (or approximate W-disjoint orthogonality), early works [9, 10] use the clustering approaches to estimate the steering vectors in the mixing matrix A, and then obtain the estimation for A. For example, a so-called DUET algorithm is proposed in [10, 11] to estimate the mixing matrix by exploiting the ratios of the time-frequency transforms of the observed mixtures under the above W-disjoint orthogonal condition.

Process. Syst. 13, 556–562 (2001) 35. H. Sawada, H. Kameoka, S. Araki, N. Ueda, Multichannel extensions of non-negative matrix factorization with complex-valued data. IEEE Trans. Audio, Speech, Lang. Process. 21(5), 971–982 (2013) 36. A. Cichocki, H. D. Kim, S. Choi, Non-negative matrix factorization with α-divergence. Pattern Recognit. Lett. 29(9), 1433–1440 (2008) 37. F. Tan, C. Févotte, Automatic relevance determination in nonnegative matrix factorization with the β-divergence. IEEE Trans. Pattern Anal.

In recent years, sparsity has been widely exploited to solve the problem of underdetermined blind source separation (UBSS), where the number of sources exceeds that of the observed mixtures. In fact, the sparsity assumption can also be satisfied by some dependent source signals. For these signals, it is possible to find a number of areas in some representation domains, where the source signals are not active, that is, signals are sparse in theses areas. The sparsity property provides a possibility for the blind separation of dependent sources.

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Blind Source Separation: Dependent Component Analysis by Yong Xiang, Dezhong Peng, Zuyuan Yang


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