Method and device for robust predictive vector quantization of linear prediction parameters in variable bit rate speech coding

TLDR: In this article, a method and device for quantizing linear prediction parameters in variable bit-rate sound signal decoding is proposed, in which at least one quantization index and information about classification of a sound signal frame corresponding to the quantization indices are received, a prediction vector is reconstructed, and a linear prediction parameter vector is produced in response to the recovered prediction error vector and the reconstructed prediction vector.

Abstract: The present invention relates to a method and device for quantizing linear prediction parameters in variable bit-rate sound signal coding, in which an input linear prediction parameter vector is received, a sound signal frame corresponding to the input linear prediction parameter vector is classified, a prediction vector is computed, the computed prediction vector is removed from the input linear prediction parameter vector to produce a prediction error vector, and the prediction error vector is quantized. Computation of the prediction vector comprises selecting one of a plurality of prediction schemes in relation to the classification of the sound signal frame, and processing the prediction error vector through the selected prediction scheme. The present invention further relates to a method and device for dequantizing linear prediction parameters in variable bit-rate sound signal decoding, in which at least one quantization index and information about classification of a sound signal frame corresponding to the quantization index are received, a prediction error vector is recovered by applying the index to at least one quantization table, a prediction vector is reconstructed, and a linear prediction parameter vector is produced in response to the recovered prediction error vector and the reconstructed prediction vector. Reconstruction of the prediction vector comprises processing the recovered prediction error vector through one of a plurality of prediction schemes depending on the frame classification information.