Product code vector quantizers for waveform and voice coding

Abstract

Memory and computation requirements imply fundamental limitations on the quality that can be achieved in vector quantization systems used for speech waveform coding and linear predictive voice coding (LPC). One approach to reducing storage and computation requirements is to organize the set of reproduction vectors as the Cartesian product of a vector codebook describing the shape of each reproduction vector and a scalar codebook describing the gain or energy. Such shape-gain vector quantizers can be applied both to waveform coding using a quadratic-error distortion measure and to voice coding using an Itakura-Saito distortion measure. In each case, the minimum distortion reproduction vector can be found by first selecting a shape code-word, and then, based on that choice, selecting a gain codeword. Several algorithms are presented for the design of shape-gain vector quantizers based on a traning sequence of data or a probabilistic model. The algorithms are used to design shape-gain vector quantizers for both the waveform coding and voice coding application. The quantizers are simulated, and their performance is compared to that of previously reported vector quantization systems.