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

By Yong Xiang, Dezhong Peng, Zuyuan Yang

This booklet offers readers a whole and self-contained set of data approximately established resource separation, together with the most recent improvement during this box. The publication provides an summary on blind resource separation the place 3 promising blind separation ideas that could take on jointly correlated resources are awarded. The e-book extra specializes in the non-negativity dependent tools, the time-frequency research established tools, and the pre-coding established tools, respectively.

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W. Lang, Towards unique solutions of non-negative matrix factorization problems by a determinant criterion. Digit. Signal Process. 21(4), 528–534 (2011) Chapter 3 Dependent Component Analysis Using Time-Frequency Analysis Abstract Sparsity is an important property shared by many kinds of signals in numerous practical applications. These signals are sparse to some extent in different representation domains, such as time domain, frequency domain or time-frequency domain. 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.

Bertin, E. Vincent, Stability analysis of multiplicative update algorithms and application to nonnegative matrix factorization. IEEE Trans. Neural Netw. 21(12), 1869–1881 (2010) 52. K. D. Sidiropoulos, A. Swami, Non-negative matrix factorization revisited: uniqueness and algorithm for symmetric decomposition. IEEE Trans. Signal Process. 62(1), 211–224 (2014) 53. L. C. Stodden, When does non-negative matrix factorization give a correct decomposition into parts? Adv. Neural Inf. Process. Syst. 16, 1141–1148 (2003) References 47 54.

For example, t stands for a time instant in the time domain, while in the TF domain, the index t denotes a sample point in the TF plain. The column vector ai in A is called as the steering vector corresponding to the ith source signal xi (t), where i = 1, 2, . . , r [7]. e. sparsity. Sparse representation provides a good way for solving the DCA problem [5, 8, 36]. Estimating the mixing matrix A is usually the first stage of the sparsity based UBSS approaches. So far, a number of blind approaches have been developed to identify the mixing matrix by exploiting the sparsity property of source signals.

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