Retrieving Sparse Patterns Using a Compressed Sensing Framework: Applications to Speech Coding Based on Sparse Linear Prediction

Daniele Giacobello, Søren Holdt Jensen, Mads Græsbøll Christensen, Manohar Murthi, Marc Moonen

Research output: Contribution to journalArticle

67 Scopus citations


Encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application in the context of speech coding based on sparse linear prediction. In particular, a compressed sensing method can be devised to compute a sparse approximation of speech in the residual domain when sparse linear prediction is involved. We compare the method of computing a sparse prediction residual with the optimal technique based on an exhaustive search of the possible nonzero locations and the well known Multi-Pulse Excitation, the first encoding technique to introduce the sparsity concept in speech coding. Experimental results demonstrate the potential of compressed sensing in speech coding techniques, offering high perceptual quality with a very sparse approximated prediction residual.

Original languageEnglish (US)
Pages (from-to)103-106
Number of pages4
JournalIEEE Signal Processing Letters
Issue number1
StatePublished - 2010



  • compressed sensing
  • Compressive sampling
  • sparse approximation
  • speech analysis
  • speech coding

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering

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