A Novel Hierarchical Decomposition Vector Quantization Method for High-Order LPC Parameters

The paper investigates vector quantization coding of high-order (e.g., 20th-50th order) linear prediction coding (LPC) parameters, and proposes a novel hierarchical decomposition vector quantization method for a scalable speech coding framework with variable orders of LPC analysis. Instead of vector quantizing the whole group of LPC parameters in the linear spectral frequency (LSF) domain directly, the proposed method decomposes the high-order LPC model into several low-order (e.g., 10th-order) LPC models, and vector quantizes them in the LSF domain separately. For the decomposition, the high-order LPC model is converted into a group of reflection coefficients at first, and then the group is split into several subgroups and converted into multiple low-order LPC models. It is shown that the proposed method is naturally suitable for a scalable coding framework where the information of the decomposed low-order LPC models can be encoded into a multi-layered bitstream and can be combined in a progressive way to recover the high-order LPC information. Experiments in a scalable coding framework with variable LPC analysis orders (10-50) reveal that, compared to a direct vector quantization scheme, the proposed method can reduce the size of the codebook and the number of coding bits significantly, and can also efficiently reduce the computation cost.