A modification of the classical vector linear prediction (VLP) problem is presented. The introduced technique called the sparse VLP (sVLP) is based on the assumption that each component of a single LSF vector is highly correlated only to a few neighboring vector components of consecutive vectors, while the correlation between distant vector components can be ignored. This leads to simplification of predictor matrices in a way that for a chosen number of neighboring components, predictor matrices obtain multidiagonal form. It is shown that prediction gain resulting from sVLP is only slightly lower than for the case of full matrix predictors but with significant reduction of computation, both for coding and predictor design.
Cite as: Petrinovic, D., Petrinovic, D. (1999) Sparse vector linear prediction matrices with multidiagonal structure. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 1483-1486, doi: 10.21437/Eurospeech.1999-337
@inproceedings{petrinovic99_eurospeech,
author={Davor Petrinovic and Davorka Petrinovic},
title={{Sparse vector linear prediction matrices with multidiagonal structure}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={1483--1486},
doi={10.21437/Eurospeech.1999-337}
}