Application of Non-uniform Sampling in Compressed Sensing for Speech Signal

  • Changqing ZhangEmail author
  • Gang Min
  • Huan Ma
  • Xian Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)


Currently, the most widely used Gaussian random observations in compressed sensing require that signals must be discrete, and the signal waveform must be known before observation, which greatly restricts the application of compressive sensing in speech. In response to this problem, this paper draws on the advantages of non-uniform sampling, constructs a non-uniform observation matrix, directly extracts the data from the signal waveform as observations, and gives a corresponding new method of reconstruction. The theoretical analysis and simulation results show that non-uniform observation can directly apply compressed sensing to analog speech signal processing, and the corresponding reconstruction method effectively enriches the means of compressive perception reconstruction.


Compressed sensing Non-uniform sampling Speech signal 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information and CommunicationNational University of Defense TechnologyXi’anChina

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