English

Dirichlet Mixture Model based VQ Performance Prediction for Line Spectral Frequency

Machine Learning 2020-02-04 v2 Sound Audio and Speech Processing Machine Learning

Abstract

In this paper, we continue our previous work on the Dirichlet mixture model (DMM)-based VQ to derive the performance bound of the LSF VQ. The LSF parameters are transformed into the Δ\DeltaLSF domain and the underlying distribution of the Δ\DeltaLSF parameters are modelled by a DMM with finite number of mixture components. The quantization distortion, in terms of the mean squared error (MSE), is calculated with the high rate theory. The mapping relation between the perceptually motivated log spectral distortion (LSD) and the MSE is empirically approximated by a polynomial. With this mapping function, the minimum required bit rate for transparent coding of the LSF is estimated.

Keywords

Cite

@article{arxiv.1808.00818,
  title  = {Dirichlet Mixture Model based VQ Performance Prediction for Line Spectral Frequency},
  author = {Zhanyu Ma},
  journal= {arXiv preprint arXiv:1808.00818},
  year   = {2020}
}

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Technical Report

R2 v1 2026-06-23T03:22:49.234Z