[1]余丰,奚吉,赵力,等.基于CS与K-SVD的欠定盲源分离稀疏分量分析[J].东南大学学报(自然科学版),2011,41(6):1127-1131.[doi:10.3969/j.issn.1001-0505.2011.06.002]
　Yu Feng,Xi Ji,Zhao Li,et al.Sparse presentation of underdetermined blind source separation based on compressed sensing and K-SVD[J].Journal of Southeast University (Natural Science Edition),2011,41(6):1127-1131.[doi:10.3969/j.issn.1001-0505.2011.06.002]
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基于CS与K-SVD的欠定盲源分离稀疏分量分析()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第6期
页码:
1127-1131
栏目:
信息与通信工程
出版日期:
2011-11-20

文章信息/Info

Title:
Sparse presentation of underdetermined blind source separation based on compressed sensing and K-SVD
作者:
余丰1; 奚吉1; 赵力1; 邹采荣1; 2
(1东南大学水声信号处理教育部重点实验室, 南京 210096)
(2佛山科学技术学院, 佛山 528000)
Author(s):
Yu Feng1; Xi Ji1; Zhao Li1; Zou Cairong1; 2
(1 Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China)
(2Foshan University, Foshan 528000, China)
关键词:
欠定盲源分离;  稀疏表示;  压缩感知
Keywords:
underdetermined blind source separation;  sparse representation;  compressed sensing
分类号:
TN912.35
DOI:
10.3969/j.issn.1001-0505.2011.06.002
摘要:
为了提高盲源分离的准确率,提出了结合压缩感知(CS)与K均值奇异值分解(K-SVD)的稀疏分量分析方法进行盲源分离.首先,分析欠定盲源分离估计源信号与压缩感知问题的等价性,建立压缩感知框架; 其次,在此框架下利用K-SVD方法训练稀疏字典; 最后利用经典追踪算法计算得到稀疏分量,结合传统的两步法,进行盲源分离.大量实验表明,该算法与其他稀疏表示方法相比获得了较好的分离效果.与传统两步法不同的是,该算法在压缩感知框架下利用K-SVD方法自适应地训练稀疏字典,求出混合信号的稀疏表示,稀疏分量分析方法的改进对盲源分离的准确率起到直接的影响作用.
Abstract:
To improve the precision of blind source separation, a method based on the compressed sensing (CS) and K-means singular value decomposition (K-SVD) is proposed. First, the equivalence between the problem of estimating the source in underdetermined blind source separation and the compressed sensing is analyzed and the framework of compressed sensing is built. Then K-SVD is used to train sparse dictionary self-adaptive under the framework. Finally the sparse component is computed using classic basis pursuit algorithm. Through lots of experiments the algorithm is proved to be a better algorithm, which inherits the advantages of sparse presentation ability and can significantly improve the precision of blind source separation. Different from traditional two steps methods , the algorithm proposed gets sparse presentation of signal taking a new way that combine CS and K-SVD, it shows that sparse presentation influences the result of blind resource separation directly.

参考文献/References:

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[2] Li Y Q, Amari S I, Cichocki A. Underdetermined blind source separation based on sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(2): 423-437.
[3] Xu T, Wang W W. A compressed sensing approach for underdetermined blind audio source separation with sparse representation[C]//IEEE/SP 15th Workshop on Statistical Signal Processing. Cardiff, UK, 2009: 493-496.
[4] Lee T W, Lewicki M S, Girolami M, et al. Blind source separation of more sources than mixtures using overcomplete representations [J]. IEEE Signal Processing Letters, 1999, 6(4): 87-90.
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[6] 石光明,刘丹华,高大化,等. 压缩感知理论及其进展[J].电子学报,2009,37(5):1070-1081.
　　Shi Guangming, Liu Danhua, Gao Dahua, et al. Advances in theory and application of compressed sensing[J]. Acta Electronica Sinica, 2009, 37(5): 1070-1081.(in Chinese)
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备注/Memo

备注/Memo:
作者简介: 余丰(1987—),女,博士生; 赵力(联系人),男,博士,教授,博士生导师,zhaoli@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(60872073,60975017,51075068)、广东省自然科学基金资助项目(10252800001000001)、东南大学水声信号处理教育部重点实验室开放研究基金资助项目(UASP1003).
引文格式: 余丰,奚吉,赵力,等.基于CS与K-SVD的欠定盲源分离稀疏分量分析[J].东南大学学报:自然科学版,2011,41(6):1127-1131. [doi:10.3969/j.issn.1001-0505.2011.06.002]
更新日期/Last Update: 2011-11-20