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Can I read Blind Source Separation for the Analysis Sparse Model on EtoBox?
Blind Source Separation for the Analysis Sparse Model by Shuang Ma; Hongjuan Zhang; Zhuoyun Miao is a Computer Science article available to read on EtoBox.
What is Blind Source Separation for the Analysis Sparse Model about?
Sparsity of the signal has been shown to be very useful for blind source separation (BSS) problem which aims at recovering unknown sources from their mixtures. In this paper, we propose a novel algorithm based on the analysis sparse constraint of the source over an adaptive analysis dictionary to address BSS problem. This method has an alternating scheme by keeping all but one unknown fixed at a time so that the dictionary, the source, and the mixing matrix are estimated alternatively. In order to make better use of the sparsity constrain, l 0 -norm is utilized directly for a more exact solution instead of its other relaxation, such as l p -norm (0\p 1). Numerical experiments show that the proposed method indeed improves the separation performance.
Who reads Blind Source Separation for the Analysis Sparse Model?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Shuang Ma; Hongjuan Zhang; Zhuoyun Miao
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Computer Science (Physical Sciences)