Speech coding using the matrix pencil

Darren M. Haddad, Tapan K. Sarkar, Andrew J. Noga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Matrix Pencils (MP) facilitate the study of differential equations resulting from oscillating systems. Certain problems in linear ordinary differential equations, such as speech processing, can be represented as the problem of finding a canonical pencil strictly equivalent to a given pencil. The MP is a direct data domain approach to estimate the signal's poles. The MP depends on only the data and is not a stochastic estimator. This approach has many benefits over a statistical approach. One benefit is that a smaller amount of data could be used to estimate the poles. The second benefit is that it results in a lower variance associated with the estimates of the parameters, which comes close to the Cramer-Rao bound [1]. In this paper, the results of recent work have been extended [2].

Original languageEnglish (US)
Title of host publicationProceedings of the 9th IASTED International Conference on Signal and Image Processing, SIP 2007
Pages115-118
Number of pages4
StatePublished - Dec 1 2007
Event9th IASTED International Conference on Signal and Image Processing, SIP 2007 - Honolulu, HI, United States
Duration: Aug 20 2007Aug 22 2007

Publication series

NameProceedings of the 9th IASTED International Conference on Signal and Image Processing, SIP 2007

Other

Other9th IASTED International Conference on Signal and Image Processing, SIP 2007
CountryUnited States
CityHonolulu, HI
Period8/20/078/22/07

Keywords

  • Audio coding
  • Eigenvalues and eigenfunctions
  • Matrix pencil

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Haddad, D. M., Sarkar, T. K., & Noga, A. J. (2007). Speech coding using the matrix pencil. In Proceedings of the 9th IASTED International Conference on Signal and Image Processing, SIP 2007 (pp. 115-118). (Proceedings of the 9th IASTED International Conference on Signal and Image Processing, SIP 2007).