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基于匹配追踪的语音压缩感知观测序列的正弦字典建模 被引量：3

A New Sinusoidal Dictionary Modeling for Compressed Sensing Measurements Based on Matching Pursuit Method
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摘要 压缩感知技术是目前信号处理领域的研究热点。文中针对压缩感知技术实际应用于语音领域时压缩率不高的问题做了研究。首先介绍了压缩感知技术的理论,接着提出了适合建模的观测矩阵的选用标准。在行阶梯观测矩阵下,本文提出了观测序列的一种新模型——基于匹配追踪算法的正弦字典模型,该模型采用稀疏分解的方法对观测序列进行建模。仿真结果表明该建模方法有效地进一步压缩了观测序列,而不明显降低恢复语音的质量。 Compressed sensing （CS） has been a hot spot for research in signal processing. But the com- pression rate of speech CS processing is far below the theoretical value, So some researches are done on this problem. Firstly, the basic theory of CS is introduced. Then, the choice criterion of the measurement matrix for measurements modeling is presented. Finally, in the row echelon measurement matrix, a new sinusoidal dictionary model is established based on matching pursuit （MP） technology, the model uses the sparse decomposition method to model the measurements. Simulation results show that the modelling method can effectively reduce the measurement sequences without significant damage to the recovered speech.
作者 严菲 杨震
机构地区 南京邮电大学宽带无线通信与传感网技术教育部重点实验室
出处 《南京邮电大学学报（自然科学版）》 北大核心 2014年第2期27-31,共5页 Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition
基金 国家自然科学基金(61271335)资助项目
关键词 压缩感知 行阶梯观测矩阵 观测序列 匹配追踪 正弦字典 建模 compressed sensing(CS) compressed sensing（CS） row echelon measurement matrix measurement sequences matching pursuits sinusoidal dictionary modeling
分类号 TN926 [电子电信—通信与信息系统]
  • 相关文献

参考文献10

  • 1DONOHO D.Compressed sensing[J].IEEE Trans on Inform Theory,2006,52(4):1289-1306.
  • 2DONOHO D,TSAIG Y.Extensions of compressed sensing[J].Signal Processing,2006,86 (3):533-548.
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  • 4叶蕾,杨震,王天荆,孙林慧.行阶梯观测矩阵、对偶仿射尺度内点重构算法下的语音压缩感知[J].电子学报,2012,40(3):429-434. 被引量：22
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二级参考文献26

  • 1D Donoho.Compressed sensing[J].IEEE Trans on InformationTheory,2006,52(4):1289-1306.
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共引文献22

同被引文献22

  • 1Donoho D L. Compressed sensing[ J]. IEEE Trans on Informa- tion Theory,2006,52 ( 4 ) : 1289-1306.
  • 2Donoho D L, Tsaig Y. Extensions of compressed sensing [ J ]. Signal Processing ,2006,86(3 ) :533-548.
  • 3Candes E J,Wakin M B. An introduction to compressive sam- piing [ J ]. IEEE Signal Processing Magazine, 2008,25 ( 2 ) : 21 -30.
  • 4Kassim L A, Khalifa O O, Gunawan T S, et al. Compressive sensing based low bit rate speech encoder[ C]//Proc of 2012 international conference on computer and communication engi- neering. [ s. 1. ]: [ s. n. ] ,2012:302-307.
  • 5Li R, Gan Z L, Cui Z G, et al. Side information extrapolation using motion- aligned auto regressive model for compressed sensing based Wyner-Ziv codec[ J]. KSII Transactions on In- ternet and Information Systems,2013,7(2) :366-385.
  • 6Lustig M, Donoho D L, Santos J M, et al. Compressed sensing MRI [ J ]. IEEE Signal Processing Magazine,2008,25 ( 2 ) : 72- 82.
  • 7Xu Tao, Wang Wenwu. A compressed sensing approach for un- derdetermined blind audio source separation with sparse repre- sentation[ C ]//Proc of IEEE/SP 15th workshop on statistical signal processing. Cardiff: IEEE,2009:493-496.
  • 8Giacobello G, Christense M G, Murthi M N, et al. Retrieving sparse patterns using a compressed sensing framework:appli- cations to speech coding based on sparse linear prediction [ J ]. IEEE Signal Processing Letters ,2010,17 ( 1 ) 103-106.
  • 9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：712
  • 10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

引证文献3

二级引证文献3

南京邮电大学学报（自然科学版）
2014年 第2期

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