By Ganesh R. Naik, Wenwu Wang
Blind resource Separation intends to document the hot result of the efforts at the learn of Blind resource Separation (BSS). The publication collects novel study principles and a few education in BSS, self reliant part research (ICA), man made intelligence and sign processing purposes. moreover, the learn effects formerly scattered in lots of journals and meetings around the globe are methodically edited and provided in a unified shape. The ebook could be of curiosity to college researchers, R&D engineers and graduate scholars in laptop technology and electronics who desire to study the center rules, tools, algorithms and functions of BSS.
Dr. Ganesh R. Naik works at collage of expertise, Sydney, Australia; Dr. Wenwu Wang works at college of Surrey, UK.
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Extra resources for Blind Source Separation: Advances in Theory, Algorithms and Applications
This joint dictionary learning and source separation framework is the focus of this chapter. , apply clustering method to solve the the ill-posed inverse problem in underdetermined model ; however, discussion on such an extension is beyond the scope of this chapter. In this chapter, we focus on overdetermined/even determined model. The remainder of this chapter is organized as follows. 2 describes the framework of the BSS problem based on dictionary learning. The recently proposed algorithm SparseBSS is introduced and compared in detail with the related benchmark algorithm BMMCA.
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13 The above conditions for each elementary test are the same as in . 14 We therefore here perform more exhaustive tests than in , where only one elementary test was performed for each set of conditions (and we avoided the complex-valued outputs mentioned below). 30 Y. Deville and A. Deville Nb. of estim. yielding real RMSE 100 90 80 70 60 BQSS−m1, K = 100 000 m BQSS − m1, K m = 10 000 BQSS − m2, K m = 100 000 BQSS − m2, K m = 10 000 BQSS − MI, K m = 100 000 BQSS − MI, K m = 10 000 50 40 30 20 10 0 3 10 5 4 10 10 number of measurements Ks Fig.
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