Titlebook: Blind Source Separation; Dependent Component Yong Xiang,Dezhong Peng,Zuyuan Yang Book 2015 The Author(s) 2015 Blind Source Separation (BSS

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書目名稱Blind Source Separation影響因子(影響力)




書目名稱Blind Source Separation影響因子(影響力)學(xué)科排名




書目名稱Blind Source Separation網(wǎng)絡(luò)公開度




書目名稱Blind Source Separation網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Blind Source Separation被引頻次




書目名稱Blind Source Separation被引頻次學(xué)科排名




書目名稱Blind Source Separation年度引用




書目名稱Blind Source Separation年度引用學(xué)科排名




書目名稱Blind Source Separation讀者反饋




書目名稱Blind Source Separation讀者反饋學(xué)科排名

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,Einführung in den Problemhorizont,chieve blind source separation, where time-frequency analysis (TFA) will be used as a powerful tool for dependent component analysis (DCA). We will also show that for those non-sparse signals whose auto-source points and cross-source points do not overlap in the TF plane, they can be separated by us

Exclaim

2191-8112 world applications.Written by leading experts in the field.IThis book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind s

在前面

https://doi.org/10.1007/978-3-658-18749-1l applications. Then, we give a brief overview of the traditional BSS methods for separating independent or uncorrelated source signals. After that, the BSS problem with mutually correlated sources are discussed, together with several mainstream BSS schemes and the corresponding algorithms.

Sinus-Node

Introduction,l applications. Then, we give a brief overview of the traditional BSS methods for separating independent or uncorrelated source signals. After that, the BSS problem with mutually correlated sources are discussed, together with several mainstream BSS schemes and the corresponding algorithms.

Ondines-curse

2191-8112 eparation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.978-981-287-226-5978-981-287-227-2Series ISSN 2191-8112 Series E-ISSN 2191-8120

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https://doi.org/10.1007/978-3-322-87373-6 before transmission such that BSS can be achieved at the receiver. Different from the method in [.], the precoding based methods do not impose any condition on the time-frequencys distributions of the sources.

治愈

Dependent Component Analysis Exploiting Nonnegativity and/or Time-Domain Sparsity,he nonnegative sparse representation (NSR) based methods, the convex geometry analysis (CGA) based methods, and the nonnegative matrix factorization (NMF) based methods. These methods either exploit the nonnegativity of the sources or both the nonnegativity and time-domain sparsity of the sources.

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