By Topic

Subsets of reflection coefficients

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
P. Broersen ; Delft University of Technology, Delft, Netherlands

Subset models of autoregressive reflection coefficients have a number of non-zero reflections in the subset while the other reflections are zero. Speech analysis and seismic exploration are areas of applications where true subset processes may be found. The estimation of subsets of reflection coefficients differs completely from any linear subset selection procedure. This paper presents three selection strategies with an increasing computational effort. An efficient algorithm can use all three strategies, but picks out the cheapest solution for the occasion. All strategies use the Weak Parameter Criterion WPC for the selection. One reason for the selection of subset models can be an adequate representation of true processes. On the other hand, selected subsets below the WPC-order give often a smaller prediction error and a closer approximation of the power spectral density than selected complete models.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '86.  (Volume:11 )

Date of Conference:

Apr 1986