Variable-dimension quantization of sinusoidal amplitudes using Gaussian mixture models

In this paper, Gaussian mixture (GM) models are used to design variable-dimension quantizers according to a weighted distortion criterion. A general method for combining a variable-to-fixed dimension transform, with GM modeling and quantization, is proposed. The method provides a convenient and efficient way to encode the amplitudes in a sinusoidal speech coder. Quantizers designed according to the proposed scheme are evaluated both according to weighted distortion criteria, and with respect to a high-rate bound approximation of the distortion. Informal listening tests suggest that the amplitudes can be encoded without subjective loss in a wideband, harmonic coder, at a rate around 40 bits per frame (for the amplitudes only).