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Weighted maximum likelihood autoregressive and moving average spectrum modeling

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2 Author(s)
Badeau, R. ; Dept. of TSI, Telecom Paris, Paris ; David, B.

We propose new algorithms for estimating autoregressive (AR), moving average (MA), and ARM A models in the spectral domain. These algorithms are derived from a maximum likelihood approach, where spectral weights are introduced in order to selectively enhance the accuracy on a predefined set of frequencies, while ignoring the other ones. This is of particular interest for modeling the spectral envelope of harmonic signals, whose spectrum only contains a discrete set of relevant coefficients. In the context of speech processing, our simulation results show that the proposed method provides a more accurate ARMA modeling of nasal vowels than the Durbin method.

Published in:

Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on

Date of Conference:

March 31 2008-April 4 2008

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