PDF-optimized LSF vector quantization based on beta mixture models

The line spectral frequencies (LSF) are known to be the most efficient representation of the linear predictive coding (LPC) parameters from both the distortion and perceptual point of view. By considering the bounded property of the LSF parameters, we apply beta mixture models (BMM) to model the distribution of the LSF parameters. Meanwhile, by following the principles of probability density function (PDF) optimized vector quantization (VQ), we derive the bit allocation strategy for the BMM. The LSF parameters are obtained from the TIMIT database and a practical VQ is designed. By taking the Bayesian information criterion (BIC), the square error (SE) and the spectral distortion (SD) as the criteria, the BMM based VQ outperforms the Gaussian mixture model based VQ with uncorrelated Gaussian component (UGMVQ) by about 1 ∼ 2 bits/vector.