Fast Search Methods for Spectral Quantization

Abstract

In this paper, we examine the computational requirements for the split-vector class of vector quantizers when applied to low-rate speech spectrum quantization. The split-vector quantization techniques are able to reduce the complexity and storage requirements of the 24-bit per frame spectral quantizer to manageable proportions. However, further dramatic reductions in computational complexity are possible, as will be demonstrated. As the fast-search algorithms reported in the literature are somewhat data dependent, it has been necessary to carefully evaluate several methods specifically for the speech coding problem. A total of six methods have been evaluated for their effectiveness in this task, and we show that a so-called “geometric” fast-search method results in a reduction in the average search time of an order of magnitude.