International Journal of Speech Technology

, Volume 21, Issue 1, pp 19–27 | Cite as

Single-channel blind source separation based on joint dictionary with common sub-dictionary

  • Linhui SunEmail author
  • Cheng Zhao
  • Min Su
  • Fu Wang


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.


Sparse representation Single-channel blind source separation Common sub-dictionary Similar atoms 



This work is supported by the National Natural Science Foundation of China (No.61501251), the Natural Science Foundation of Jiangsu Province (BK20140891) and the Scientific Research Foundation of Nanjing University of Posts and Telecommunications (NY214038).


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of Telecommunications & Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Key Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of EducationNanjing University of Posts and TelecommunicationsNanjingChina

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