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.

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

References in corpus (1)

• A comparative study between linear and nonlinear speech prediction

Cited by in corpus (3)

• Adaptive hybrid speech coding with a MLP LPC structure
• Non-Linear Speech coding with MLP, RBF and Elman based prediction
• A new subband non linear prediction coding algorithm for narrowband speech signal: The nADPCMB MLT coding scheme