Weighted vector quantization of harmonic spectral magnitudes for very low-bit-rate speech coding

Abstract

Harmonic coding is a very powerful technique for the coding of speech at very low bit rates; and the efficient coding of spectral magnitudes sampled at harmonic frequencies is the key to obtaining good coded-speech quality. This paper presents a weighted vector quantization method for spectral vectors composed of a variable number of harmonic magnitudes. It is based on simple, efficient linear dimension conversion and employs a weighted distortion measure that exploits the human auditory sense. A codebook training algorithm using the weighting matrix is also presented. Finally, a low-complexity VQ codebook search technique based on pre-selection is described that reduces the computational complexity to less than 10% of that of an exhaustive search, without perceptible loss of quality. The proposed quantization scheme is used in Harmonic Vector eXcitation Coding (HVXC), which is a very low-bit-rate speech coding algorithm that combines harmonic and stochastic vector representations of LPC residual signals. Due to the high efficiency of this VQ scheme, HVXC provides good communication-quality speech at bit rates as low as 2–4 kbit/s, and was adopted as the ISO/IEC International Standard for MPEG-4 Audio.