Speech coding based on sparse linear prediction

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

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations


This paper describes a novel speech coding concept created by introducing sparsity constraints in a linear prediction scheme both on the residual and on the prediction vector. The residual is efficiently encoded using well known multi-pulse excitation procedures due to its sparsity. A robust statistical method for the joint estimation of the short-term and long-term predictors is also provided by exploiting the sparse characteristics of the predictor. Thus, the main purpose of this work is showing that better statistical modeling in the context of speech analysis creates an output that offers better coding properties. The proposed estimation method leads to a convex optimization problem, which can be solved efficiently using interior-point methods. Its simplicity makes it an attractive alternative to common speech coders based on minimum variance linear prediction.

Original languageEnglish (US)
Pages (from-to)2524-2528
Number of pages5
JournalEuropean Signal Processing Conference
StatePublished - Dec 1 2009
Event17th European Signal Processing Conference, EUSIPCO 2009 - Glasgow, United Kingdom
Duration: Aug 24 2009Aug 28 2009

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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