Nonlinear prediction with neural nets in ADPCM
Abstract
In the last years there has been a growing interest for nonlinear speech models. Several works have been published revealing the better performance of nonlinear techniques, but little attention has been dedicated to the implementation of the nonlinear model into real applications. This work is focused on the study of the behaviour of a nonlinear predictive model based on neural nets, in a speech waveform coder. Our novel scheme obtains an improvement in SEGSNR between 1 and 2 dB for an adaptive quantization ranging from 2 to 5 bits.
Cite
@article{arxiv.2203.11612,
title = {Nonlinear prediction with neural nets in ADPCM},
author = {Marcos Faundez-Zanuy and Francesc Vallverdu and Enric Monte},
journal= {arXiv preprint arXiv:2203.11612},
year = {2022}
}
Comments
4 pages, published in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181) Seattle, WA, USA. arXiv admin note: text overlap with arXiv:2203.01818