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
N1 - Funding Information:
The work of D. Giacobello was supported by the Marie Curie EST-SIGNAL Fellowship under Contract MEST-CT-2005-021175 and was carried out at the Department of Electronic Systems, Aalborg University. The work of T. L. Jensen was supported by The Danish Council for Strategic Research under grant number 4005-00122.
Publisher Copyright:
© 2018 IEEE.
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 - Sparse linear prediction
KW - convex optimization
KW - real-time implementation
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
T2 - 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Y2 - 28 October 2018 through 31 October 2018
ER -