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一种新的基于稀疏分解的单通道混合语音分离方法 被引量:5

A New Single-Channel Speech Separation Method Based on Sparse Decomposition
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摘要 论文以新的语音信号稀疏基—准KLT基的构造为基础,提出了一种新的基于稀疏分解的单通道混合语音分离方法.论文首先以理想准KLT基的构造为基础,从理论上提出并证明了基于各源语音信号的理想准KLT基,利用lexp(0)-范数优化算法,可实现单通道混合语音的完美分离.鉴于单通道混合语音分离时,无法精确求取各源语音信号的理想准KLT基,论文提出先基于正交匹配追踪算法,以混合语音信号为已知条件,构造各源语音信号的正交匹配追踪模板匹配准KLT基,再由lexp(0)-范数优化算法来分离单通道混合语音.仿真实验表明论文所提理论的正确性,和基于正交匹配追踪模板匹配准KLT基来分离单通道混合语音信号的有效性. This paper proposes a new single-channel speech separation method based on sparse decomposition. The new method is based on the construction algorithms of quasi-KLT bases proposed in this paper.First,it is proved that based on the ideal quasi-KLT bases generated from the sources,both sources can be perfectly separated from a single mixture by l exp(0) optimization. Then, considering the fact that the ideal quasi-KLT bases cannot be obtained in practice, orthogonal matching pursuit (OMP) template-matching quasi-KLT bases generated from the single mixture are first proposed, and then perform single-channel speech separation by 1 exp(0) optimization using the OMP template-matching quasi-KLT bases. Finally, simulation results demonstrating the perfect separation of both sources from a single-channel speech mixture by 1 exp(0) optimization using ideal quasi-KLT bases, as well as the effective performance of single-channel separation of two speech sources using the OMP template-matching quasi-KLT bases are presented.
出处 《电子学报》 EI CAS CSCD 北大核心 2012年第4期762-768,共7页 Acta Electronica Sinica
基金 国家自然科学基金(No.60971129) 中国博士后基金(No.20100481167) 南京农业大学工学院引进人才科研启动基金(No.rcqd11-02)
关键词 语音分离 稀疏分解 lexp(0)-范数优化 正交匹配追踪 KARHUNEN-LOEVE变换 speech separation sparse decomposition 1 exp(0) optimization orthogonal matching pursuit (OMP) Karhunen- Loeve transform (KLT)
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参考文献11

  • 1Schmidt M N, Olsson R K. Linear regression on sparse features for single-channel speech separation[ A]. IEEE Workshop on App/ications of Signal Processing to Audio and Acoustics[ C]. NY, USA, 2007.26 - 29. 被引量:1
  • 2Pearlmutter B, Olsson R. Linear program differentiation for sin- gle - channel speech separation[A]. 16th IEEE, Signal Process- ing Society Workshop on Machine learning for Signal Process- ing[ C]. Maynooth, Ireland, 2006.421 - 426. 被引量:1
  • 3Nakashizuka N, Okumura H, figuni Y. Single-channel speech separation by using a sparse decomposition with periodic struc- ture[ A ]. 2008 International Symposium on Intelligent Signal Processing and Communications Systems[ C ]. Bangkok, Thai- land, 2008.1 - 4. 被引量:1
  • 4Elad M, Bruckstein A, A generalized uncertainty principle and sparse representation in pairs of bases[ J]. IEEE Transactions on Information Theorv, 2002,48: 2558 - 2567. 被引量:1
  • 5郭海燕,杨震.基于近似KLT域的语音信号压缩感知[J].电子与信息学报,2009,31(12):2948-2952. 被引量:32
  • 6Donoho D L. For Most Large Underdetermined Systems of E- quations, the Minimal 11 - norm Near- Solution Approximates the Sparsest Near-Solution[ R]. http://www-stat, stanford, edu / - donoho/Reports/2004. 被引量:1
  • 7Y Pail, R Rezaiifar, and P Krishnaprasad. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition[ A]. Proceedings of 27th Annual Asilo- mar Conference on Signals, Systems and Computers[ C]. CA, USA, 1993.40 - 44. 被引量:1
  • 8石光明,刘丹华,高大化,刘哲,林杰,王良君.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081. 被引量:710
  • 9孙玉宝,吴敏,韦志辉,肖亮,冯灿.基于稀疏表示的脑电棘波检测算法研究[J].电子学报,2009,37(9):1971-1976. 被引量:7
  • 10Gil-Jin Jang, Te-Won Lee, Yung-Hwan Oh. Single-channel signal separation using time-domain basis functiom[ J]. IEEE Signal Processing Letters,2003, 10(6) : 168 - 171. 被引量:1

二级参考文献103

  • 1张春梅,尹忠科,肖明霞.基于冗余字典的信号超完备表示与稀疏分解[J].科学通报,2006,51(6):628-633. 被引量:71
  • 2R Baraniuk.A lecture on compressive sensing[J].IEEE Signal Processing Magazine,2007,24(4):118-121. 被引量:1
  • 3Guangming Shi,Jie Lin,Xuyang Chen,Fei Qi,Danhua Liu and Li Zhang.UWB echo signal detection with ultra low rate sampling based on compressed sensing[J].IEEE Trans.On Circuits and Systems-Ⅱ:Express Briefs,2008,55(4):379-383. 被引量:1
  • 4Cand,S E J.Ridgelets:theory and applications[I)].Stanford.Stanford University.1998. 被引量:1
  • 5E Candès,D L Donoho.Curvelets[R].USA:Department of Statistics,Stanford University.1999. 被引量:1
  • 6E L Pennec,S Mallat.Image compression with geometrical wavelets[A].Proc.of IEEE International Conference on Image Processing,ICIP'2000[C].Vancouver,BC:IEEE Computer Society,2000.1:661-664. 被引量:1
  • 7Do,Minh N,Vetterli,Martin.Contourlets:A new directional multiresolution image representation[A].Conference Record of the Asilomar Conference on Signals,Systems and Computers[C].Pacific Groove,CA,United States:IEEE Computer Society.2002.1:497-501. 被引量:1
  • 8G Peyré.Best Basis compressed sensing[J].Lecture Notes in Ccmputer Science,2007,4485:80-91. 被引量:1
  • 9V Temlyakov.Nonlinear Methods of Approximation[R].IMI Research Reports,Dept of Mathematics,University of South Carolina.2001.01-09. 被引量:1
  • 10S Mallat,Z Zhang.Matching pursuits with time-frequency dictionaries[J].IEEE Trans Signal Process,1993,41(12):3397-3415. 被引量:1

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