Nonlinear prediction is a natural way to increase the quality of speech coders. Several approaches have been recently proposed in this direction ([1,2,3,4] are some examples) and most of them use neural networks as predictors. Nevertheless, the computational cost due to the network training is very high, since it usally involves a gradient descent-based nonlinear optimization process. In this paper we propose some improvements of our previous work reported in [3], all of them aiming at reducing the computational cost. Our predictor can be used in CELP-type coders and provides a 0.6 dB increase of the SEGSNR with respect to conventional CELP coders.
Cite as: Peláez-Moreno, C., Díaz-de-María, F. (1999) Backward adaptive RBF-based hybrid predictors for CELP-type coders at medium bit-rates. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 1475-1478, doi: 10.21437/Eurospeech.1999-335
@inproceedings{pelaezmoreno99_eurospeech,
author={C. Peláez-Moreno and F. Díaz-de-María},
title={{Backward adaptive RBF-based hybrid predictors for CELP-type coders at medium bit-rates}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={1475--1478},
doi={10.21437/Eurospeech.1999-335}
}