Dirichlet mixture modeling to estimate an empirical lower bound for LSF quantization
2014 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 104, 291-295 p.Article in journal (Refereed) Published
The line spectral frequencies (LSFs) are commonly used for the linear predictive/autoregressive model in speech and audio coding. Recently, probability density function (PDF)-optimized vector quantization (VQ) has been studied intensively for quantization of LSF parameters. In this paper, we study the VQ performance bound of the LSF parameters. The LSF parameters are transformed to the Delta LSF domain and the underlying distribution of the Delta LSF parameters is modeled by a Dirichlet mixture model (DMM) with a finite number of mixture components. The quantization distortion, in terms of the mean squared error (MSE), is calculated with high rate theory. For LSF quantization, 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 (an empirical lower bound) for transparent coding of the LSF under DMM modeling is derived.
Place, publisher, year, edition, pages
2014. Vol. 104, 291-295 p.
Line spectral frequency, Vector quantization, Performance bound, Dirichlet mixture model
IdentifiersURN: urn:nbn:se:kth:diva-148327DOI: 10.1016/j.sigpro.2014.04.023ISI: 000338392500029ScopusID: 2-s2.0-84901047826OAI: oai:DiVA.org:kth-148327DiVA: diva2:736326
FunderEU, FP7, Seventh Framework Programme, 612212
QC 201408062014-08-062014-08-052014-08-06Bibliographically approved