Re-estimation of linear predictive parameters in sparse linear prediction

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this work, we propose a novel scheme to re-estimate the linear predictive parameters in sparse speech coding. The idea is to estimate the optimal truncated impulse response that creates the given sparse coded residual without distortion. An all-pole approximation of this impulse response is then found using a least square approximation. The all-pole approximation is a stable linear predictor that allows a more efficient reconstruction of the segment of speech. The effectiveness of the algorithm is proved in the experimental analysis.

Original languageEnglish (US)
Title of host publicationConference Record - 43rd Asilomar Conference on Signals, Systems and Computers
Pages1770-1773
Number of pages4
DOIs
StatePublished - 2009
Event43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 1 2009Nov 4 2009

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other43rd Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/1/0911/4/09

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Giacobello, D., Murthi, M. N., Christensen, M. G., Jensen, S. H., & Moonen, M. (2009). Re-estimation of linear predictive parameters in sparse linear prediction. In Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers (pp. 1770-1773). [5470202] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2009.5470202