期刊文献+

基于声源时延估计的欠定盲分离方法 被引量:1

Underdetermined Blind Separation Based on Sound Source Time-Delay Estimation
下载PDF
导出
摘要 提出一种基于声源时延估计的二元时频掩蔽方法,通过三个接收信号实现多于多个语音源信号的欠定盲分离。利用语音信号的W-分离正交性,在时频域估计各个源信号到达接收阵列的相对时延序列;进而基于信号时延序列的估计,采用最大似然算法将时频域划分为与源信号个数相同的互不重叠的时频点集合,每个集合(近似)只包含一个源信号的所有时频分量;再通过二元时频掩蔽依次恢复出各集合所对应的源信号。该方法性能通过主观试听得到了验证,其分段信噪比增益至少为13 dB。较之欠定解混迭估计技术DUET,本文方法得到的分离信号与实际声源信号的相异度降低约3 dB。 Based on time-delay estimation, a time-frequency masking method is proposed for underdetermined blind source separation. The method can realize the blind separation more than 3 source signals by using only 3 received array elements. Firstly, relative time-delay sequences of all sources are estimated in time-frequency domain by virtue of the W-disjoint orthogonality of speech signals. Secondly, based on the estimated time-delay sequences, the maximum likelihood method is used to estimate the support domain of each signal. The timefrequency components in each support domain belong to only one signal approximatively, and different support domains are mutually disjoint. Finally, the time-frequency representation of each signal is obtained by the time-frequency masking, and then the time-domain source signals are retrieved. The experiments illustrate that the method is validated by the informal subjective measure, and the gain of segment signal-to-noise ratio is at least 13dB. Compared with the degenerate unmixing estimation technique, the separation performance of the proposed method improves about 3dB measured by signal dissimilarities.
出处 《数据采集与处理》 CSCD 北大核心 2009年第6期703-708,共6页 Journal of Data Acquisition and Processing
基金 国家自然科学基金(60672128 60702057)资助项目 国家高技术研究发展计划"八六三"计划(2007AA01Z288)资助项目
关键词 欠定盲分离 时延估计 W-分离正交性 最大似然 时频掩蔽 underdetermined blind separation time-delay estimation W-disjoint orthogonality maximum likelihood time-frequency masking
  • 相关文献

参考文献8

  • 1李小军,朱孝龙,张贤达.盲信号分离研究分类与展望[J].西安电子科技大学学报,2004,31(3):399-404. 被引量:33
  • 2鲁晓丹,张立明.改进的在线自然语音卷积混合信号时域盲分离方法[J].数据采集与处理,2007,22(2):138-143. 被引量:2
  • 3Cardoso J F, Souloumiac A. Blind beanaforming for non-Gaussian signals[J]. lEE Proceedings-F, 1993, 140(6) : 362-370.
  • 4Feng D Z,Zhang X D,Bao Z. An efficient multistage decomposition approach for independent components [J]. Signal Processing, 2003,83 ( 1 ) : 181-197.
  • 5Yilmaz O,Richard S. Blind separation of speech mixture via time-frequency masking [J]. IEEE Transactions on Signal Processing, 2004,32(7) : 1830-1847.
  • 6Aissa-EI-Bey A, Linh-Trung N, Abed-Meraim K, et al. Underdetermined blind separation of nondisjoint sources in the time-frequency domain [J. IEEE Transactions on Signal Processing, 2007,55(3) : 897- 907.
  • 7Brandstein M, Ward D. Microphone arrays: signal processing technklues and applications[M].[S.l. ]. Springer,2001:25.
  • 8孟静,许刚.语音增强算法评估的研究[J].计算机工程,2006,32(24):223-225. 被引量:6

二级参考文献43

  • 1张贤达,保铮.盲信号分离[J].电子学报,2001,29(z1):1766-1771. 被引量:210
  • 2Girolami M. Self-organising Neural Networks: Independent Component Analysis and Blind Source Separation[M]. London: SpringerVerlag. 1999.
  • 3Haykin S. Unsupervised Adaptive Filtering, Vol Ⅰ: Blind Source Separation[M]. New York: Wiley, 2000.
  • 4Hyvarinen A, Karhunen J, Oja E. Independent Component Analysis[M]. New York: Wiley, 2001.
  • 5Tong L, Liu R W, Soon V C, et al. Indeterminacy and Identifiability of Blind Identification[J]. IEEE Trans on Circuits and Systems,1991,38(5): 499-509.
  • 6Comon P. Independent Component Analysis, a New Concept? [J]. Signal Processing, 1994, 36(3): 287-314.
  • 7Amari S, Cichocki A. Adaptive Blind Signal Processing: Neural Network Approaches[J]. Proc IEEE, 1998, 86(10): 2026-2048.
  • 8Cardoso J F. Blind Signal Separation: Statistical Principles[J]. Proc IEEE, 1998, 86(10): 2009-2025.
  • 9Moreau E, Macchi O. High-order Contrasts for Self-adaptive Source Separation[J]. Int J of Adaptive Control and Signal Processing,1996, 10(1): 19-46.
  • 10Hyvarinen A. Survey on Independent Component Analysis[J]. Neural Computing, 1999, 2(1): 94-128.

共引文献38

同被引文献14

  • 1刘秋菊,王仲英,刘素华.基于遗传模拟退火算法的模糊聚类方法[J].微计算机信息,2006,22(02Z):270-272. 被引量:18
  • 2肖明,谢胜利,傅予力.基于频域单源区间的具有延迟的欠定盲分离[J].电子学报,2007,35(12):2279-2283. 被引量:20
  • 3Li Y Q, Amari S, Cichocki A, et al. Underdetermined blind source separation based on sparse representation[J]. IEEE Transaction on Signal Processing, 2006, 54 (2) : 423-437.
  • 4Zibulevsky M, Pearlmutter B A. Blind source separation by sparse decomposition[J]. Neural Computation, 2001, 13(4) :863-882.
  • 5Georgiev P, Theis F, el al. Sparse component analysis and blind source separation of underdetermined mixtures[J]. IEEE Transaction on Neural Networks, 2005, 16(4):992-996.
  • 6Abrard F, Deville Y. A time-frequency blind signal separation method application to underdetermined mixtures of dependent sources[J]. Signal Processing, 2005(85) :1389-1403.
  • 7Bofill P, Zibulevsky M. Underdetermined blind source separation using sparse representations[J]. Signal Processing, 2001, 81(11) : 2353-2362.
  • 8Yilmaz O, Rickard S. Blind separation of speech mixtures via time-frequency masking[J]. IEEE Transaction on Signal Pro- cessing, 2004, 52(7):1830-1847.
  • 9Theis F J, Lang E W, Puntonet C G. A geometric algorithm for overcomplete linear ICA[J]. Neurocomputing, 2004, 56: 381-398.
  • 10Kim S Y, Chang D Y. Underdetermined blind source separation based on subspace representation[J]. IEEE Transaction on Signal Processing, 2009, 57(7) : 2604-2614.

引证文献1

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部