Friday October 21, 2010, 11-12.00, Room 3721, Lindstedtsv. 25

Per Enqvist Optimization and Systems Theory

A class of robust power spectral estimation methods
based on approximative moment matching is presented.
Most methods for spectral estimation are based on
asymptotic results that holds when the number of data points
is large.
In speech processing, where the number of data points
is rather small, a maximum entropy based
covariance matching method
is the method of choice in most applications.
If there is an underlying model generating the data, and the
number of data points grows large, the
moment matching method can be used to
identify the generating model *if* it lies
in the considered model class.
However, for generic data the situation is different;
The estimated
model should be a best approximation of the true
generating system, and any estimate of the moments
used for the matching will have errors
since the data available is limited.
To deal with this practical situation an approximative
moment matching method based on convex optimization
has been designed. In fact, a whole class of
methods based on a similar approach have been developed.
The moments that are matched can be covariances and
cepstrum parameters, but also generalizations of these.
The level of regularization can also be controlled by
choosing a design parameter.

Calendar of seminars