Gaussian-mixture modeling of lattice-based spherical vector quantization performance in transform audio coding
Wisarn Patchoo, T.R. Fischer · 2010
A block-based Gaussian mixture model (GMM) is used to model the distribution of transform audio data to be encoded using lattice-based spherical vector quantization (LSVQ). The expectation-maximization algorithm is used to design the GMM to model the marginal density of the transform coefficients and the vector energy density. A GMM-based rate-distortion function is derived and shown to closely match the observed spherical VQ performance. The LSVQ transform audio coding performance is characterized for the best lattices known in 4, 8, 16, and 32 dimensions.