Memory-Based Vector Quantization of LSF Parameters by a Power Series Approximation

Abstract

In this paper, memory-based quantization is studied in detail. We propose a new framework, power series quantization (PSQ), for memory-based quantization. With linear spectral frequency (LSF) quantization as the application, several common memory-based quantization methods (FSVQ, predictive VQ, VPQ, safety-net, etc.) are analyzed and compared with the proposed method, and it is shown that the proposed method performs better than all other tested methods. The proposed PSQ method is fully general, in that it can simulate all other memory-based quantizers if it is allowed unlimited complexity