Abstract
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 language | English (US) |
|---|---|
| Title of host publication | European Signal Processing Conference |
| Pages | 2524-2528 |
| Number of pages | 5 |
| State | Published - 2009 |
| Event | 17th European Signal Processing Conference, EUSIPCO 2009 - Glasgow, United Kingdom Duration: Aug 24 2009 → Aug 28 2009 |
Other
| Other | 17th European Signal Processing Conference, EUSIPCO 2009 |
|---|---|
| Country | United Kingdom |
| City | Glasgow |
| Period | 8/24/09 → 8/28/09 |
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ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Speech coding based on sparse linear prediction. / Giacobello, Daniele; Christensen, Mads Græsbøll; Murthi, Manohar; Jensen, Søren Holdt; Moonen, Marc.
European Signal Processing Conference. 2009. p. 2524-2528.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Speech coding based on sparse linear prediction
AU - Giacobello, Daniele
AU - Christensen, Mads Græsbøll
AU - Murthi, Manohar
AU - Jensen, Søren Holdt
AU - Moonen, Marc
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:84863764869
SP - 2524
EP - 2528
BT - European Signal Processing Conference
ER -