Recursive LPC spectrum coding-a classified VQ approach

We propose a recursive coding scheme for spectral parameters. The coder utilizes a Gaussian mixture model describing the PDF of pairs of source vectors. From this model we can, at each coding instant, calculate a conditional PDF for the vector to be coded given the previously quantized vector. Knowing this conditional PDF, we can use high rate theory to design an optimal codebook. To reduce complexity we employ a classified vector quantizer structure. The mixture components with highest probability around the incoming vector are detected and the corresponding codebooks are searched. A pair of indices, representing the chosen component and codevector, is transmitted. Experiments show a small degradation in performance due to the imposed structural constraint. When compared with a standard predictive vector quantization (PVQ), slightly superior performance, both objectively and subjectively, is observed. Further, close to transparent spectral coding is achieved at 16 bits per frame when employed in a sinusoidal vocoder