Thursday, November 13, 1997
4:30-5:30 pm
1200 EECS
abstract-
Time-frequency (t-f)
analysis has clearly reached a certain maturity. One can
now often provide striking visual representations of the
joint time-frequency energy representation of signals.
However, it has been difficult to take advantage of this
rich source of information concerning the signal, especially
for multidimensional signals. Properly constructed
time-frequency distributions enjoy many desirable
properties. Attempts to incorporate t-f analysis results
into pattern recognition schemes have not been notably
successful to date. Aided by Cohen's scale transform one
may construct representations from the t-f results which are
highly useful in pattern classification. Such methods can
produce two dimensional representations which are invariant
to time-shift, frequency-shift and scale changes. Even so,
remaining extraneous variations often defeat the pattern
classification approach. This paper presents a method based
on noise subspace concepts. The noise subspace enhancement
allows one to separate the desired invariant forms from
extraneous variations, yielding much improved classification
results. Examples from sound classification and radar
backscatter are discussed.
Biosketch -
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