Non-Linear Speech coding with MLP, RBF and Elman based prediction

04/05/2022
by   Marcos Faundez-Zanuy, et al.
0

In this paper we propose a nonlinear scalar predictor based on a combination of Multi Layer Perceptron, Radial Basis Functions and Elman networks. This system is applied to speech coding in an ADPCM backward scheme. The combination of this predictors improves the results of one predictor alone. A comparative study of this three neural networks for speech prediction is also presented.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/04/2022

Nonlinear Vectorial Prediction with Neural Nets

In this paper we propose a nonlinear vectorial prediction scheme based o...
research
03/24/2022

A new subband non linear prediction coding algorithm for narrowband speech signal: The nADPCMB MLT coding scheme

This paper focuses on a newly developed transparent nADPCMB MLT speech c...
research
03/31/2022

A comparative study between linear and nonlinear speech prediction

This paper is focused on nonlinear prediction coding, which consists on ...
research
06/20/2018

Reinforcement Learning using Augmented Neural Networks

Neural networks allow Q-learning reinforcement learning agents such as d...
research
03/07/2022

Non-linear predictive vector quantization of speech

In this paper we propose a Non-Linear Predictive Vector quantizer (PVQ) ...
research
03/03/2022

Nonlinear predictive models computation in ADPCM schemes

Recently several papers have been published on nonlinear prediction appl...
research
11/04/2022

Neural Feature Predictor and Discriminative Residual Coding for Low-Bitrate Speech Coding

Low and ultra-low-bitrate neural speech coding achieves unprecedented co...

Please sign up or login with your details

Forgot password? Click here to reset