Abstract
An information-theory motivation for considering a contour-gain codebook structure is given and an iterative contour-gain vector quantizer (CGVQ) algorithm allowing optimization of the shape codebook for a fixed-gain codebook is described. Numerical results are presented for CGVQ encoding of first-, second-, and tenth-order Gauss-Markov sources, and a clear improvement over SGVQ (shape-gain vector quantizer) performance is demonstrated. Numerical results are also presented for CGVQ waveform encoding of speech, and again an improvement over SGVQ performance is demonstrated. The perceptual quality of the encoded speech was roughly equivalent for the two models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
