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- Title
Single-channel blind source separation based on joint dictionary with common sub-dictionary.
- Authors
Sun, Linhui; Zhao, Cheng; Su, Min; Wang, Fu
- Abstract
The cross projection engenders when mixed speech signal is represented over joint dictionary because of the bad distinguishing ability of joint dictionary in single-channel blind source separation (SBSS) using sparse representation theory, which leads to bad separation performance. A new algorithm of constructing joint dictionary with common sub-dictionary is put forward in this paper to this problem. The new dictionary can effectively avoid being projected over another sub-dictionary when a source signal is represented over joint dictionary. In the new algorithm, firstly we learn identify sub-dictionaries using source speech signals corresponding to each speaker. And then we discard similar atoms between two identity sub-dictionaries and construct a common sub-dictionary using these similar atoms. Finally, we combine those three sub-dictionaries together into a joint dictionary. The Euclidean distance among two atoms is used to measure the correlation of them in different identity sub-dictionaries, and similar atoms are searched based on the correlation. In testing stage, each source can be reconstructed with the projection coefficients corresponding to individual sub-dictionary and the common sub-dictionary. Contrast experiments tested in speech database show that the algorithm proposed in this paper performs better, when the Signal-to-Noise Ratio (SNR) is used to measure separation effect. The algorithm set out in this paper has lower time complexity as well.
- Subjects
BLIND source separation; EUCLIDEAN distance; SIGNAL-to-noise ratio; COMPUTER algorithms; COMPUTATIONAL complexity
- Publication
International Journal of Speech Technology, 2018, Vol 21, Issue 1, p19
- ISSN
1381-2416
- Publication type
Article
- DOI
10.1007/s10772-017-9469-2