Improving CELP coders by backward adaptive non-linear prediction
✍ Scribed by Fernando Díaz-de-María; Aníbal R. Figueiras-Vidal
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 218 KB
- Volume
- 11
- Category
- Article
- ISSN
- 0890-6327
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✦ Synopsis
Non-linear prediction is a natural way to increase the quality of speech coders. In particular, low-delay CELP-type coders can incorporate this improvement because the predictor adaptation is backward. Consequently, there is the possibility of using neural networks as predictors, since their weights (usually a larger number than required in the linear approach) do not have to be transmitted. We apply a radial basis function (RBF) network for this purpose since it computes a regularized solution to the prediction problem. As a result, the stability of the non-linear autoregressive synthesis system can be guaranteed. Investigations of how to combine non-linear predictors with linear predictors indicate that a cascade of an RBF network and a linear filter is a suitable selection since it provides good results and its application to analysis-bysynthesis coders results in large computational advantages with respect to the parallel configuration. This hybrid predictor has been tested for a low-delay code-excited predictive coder, providing an average improvement of 0•4 dB with respect to a CELP coder. Additionally, subjective listening tests give the proposed coder a slight preference over the CELP coder. These results are encouraging because we consider that the proposed coder can be implemented in real time after some improvements, which are detailed as the subject of further work.