ADPCM with nonlinear prediction

TL;DR

This paper explores ADPCM speech coding using neural network-based nonlinear predictors, achieving 1-2.5dB improvements in SEGSNR over traditional linear methods.

Contribution

It introduces nonlinear prediction schemes with neural nets for ADPCM, enhancing speech coding performance beyond classical linear approaches.

Findings

Neural network-based nonlinear predictors improve SEGSNR by 1-2.5dB.

Discusses block-adaptive and sample-adaptive nonlinear prediction methods.

Demonstrates the potential of nonlinear techniques in speech coding.

Abstract

Many speech coders are based on linear prediction coding (LPC), nevertheless with LPC is not possible to model the nonlinearities present in the speech signal. Because of this there is a growing interest for nonlinear techniques. In this paper we discuss ADPCM schemes with a nonlinear predictor based on neural nets, which yields an increase of 1-2.5dB in the SEGSNR over classical methods. This paper will discuss the block-adaptive and sample-adaptive predictions.