Abstract
In this work, we propose a novel approach to speech coding by rewriting the nonlinear analysis-by-synthesis linear prediction scheme as a convex problem. This allows for determining trade-offs between, on one hand, the reconstruction error and, on the other, the sparsity of the predictor and the residual used to parametrize the speech signal. Differently from traditional coding schemes where the parameters are chosen throughout multiple optimization stages, our scheme produces a one-shot parametrization of a speech segment that intrinsically takes into consideration the voiced or unvoiced nature of a speech segment providing a better balance between residual and predictor and, consequently, a more appropriate bit allocation.
| Original language | English (US) |
|---|---|
| Title of host publication | Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 |
| Editors | Michael B. Matthews |
| Publisher | IEEE Computer Society |
| Pages | 1947-1952 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538692189 |
| DOIs | |
| State | Published - Feb 19 2019 |
| Event | 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States Duration: Oct 28 2018 → Oct 31 2018 |
Publication series
| Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
|---|---|
| Volume | 2018-October |
| ISSN (Print) | 1058-6393 |
Conference
| Conference | 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 |
|---|---|
| Country | United States |
| City | Pacific Grove |
| Period | 10/28/18 → 10/31/18 |
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Keywords
- convex optimization
- real-time implementation
- Sparse linear prediction
- speech coding
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications
Cite this
Revisiting the Linear Prediction Analysis-by-Synthesis Speech Coding Paradigm Using Real-Time Convex Optimization. / Giacobello, Daniele; Murthi, Manohar; Jensen, Tobias Lindstrom; Christensen, Mads Grasboll.
Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. ed. / Michael B. Matthews. IEEE Computer Society, 2019. p. 1947-1952 8645448 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Revisiting the Linear Prediction Analysis-by-Synthesis Speech Coding Paradigm Using Real-Time Convex Optimization
AU - Giacobello, Daniele
AU - Murthi, Manohar
AU - Jensen, Tobias Lindstrom
AU - Christensen, Mads Grasboll
PY - 2019/2/19
Y1 - 2019/2/19
N2 - In this work, we propose a novel approach to speech coding by rewriting the nonlinear analysis-by-synthesis linear prediction scheme as a convex problem. This allows for determining trade-offs between, on one hand, the reconstruction error and, on the other, the sparsity of the predictor and the residual used to parametrize the speech signal. Differently from traditional coding schemes where the parameters are chosen throughout multiple optimization stages, our scheme produces a one-shot parametrization of a speech segment that intrinsically takes into consideration the voiced or unvoiced nature of a speech segment providing a better balance between residual and predictor and, consequently, a more appropriate bit allocation.
AB - In this work, we propose a novel approach to speech coding by rewriting the nonlinear analysis-by-synthesis linear prediction scheme as a convex problem. This allows for determining trade-offs between, on one hand, the reconstruction error and, on the other, the sparsity of the predictor and the residual used to parametrize the speech signal. Differently from traditional coding schemes where the parameters are chosen throughout multiple optimization stages, our scheme produces a one-shot parametrization of a speech segment that intrinsically takes into consideration the voiced or unvoiced nature of a speech segment providing a better balance between residual and predictor and, consequently, a more appropriate bit allocation.
KW - convex optimization
KW - real-time implementation
KW - Sparse linear prediction
KW - speech coding
UR - http://www.scopus.com/inward/record.url?scp=85062993031&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062993031&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2018.8645448
DO - 10.1109/ACSSC.2018.8645448
M3 - Conference contribution
AN - SCOPUS:85062993031
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1947
EP - 1952
BT - Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
A2 - Matthews, Michael B.
PB - IEEE Computer Society
ER -