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dc.contributor.authorJones, Douglas L.
Baraniuk, Richard G.
dc.creatorJones, Douglas L.
Baraniuk, Richard G. 2007-10-31T00:48:22Z 2007-10-31T00:48:22Z 1995-10-01 2004-01-09
dc.identifier.citation D. L. Jones and R. G. Baraniuk, "An Adaptive Optimal-Kernel Time-Frequency Representation," IEEE Transactions on Signal Processing, vol. 43, no. 10, 1995.
dc.description Journal Paper
dc.description.abstract Time-frequency representations with fixed windows or kernels figure prominently in many applications, but perform well only for limited classes of signals. Representations with signal- dependent kernels can overcome this limitation. However, while they often perform well, most existing schemes are block-oriented techniques unsuitable for on-line implementation or for tracking signal components with characteristics that change with time. The time-frequency representation developed here, based on a signal-dependent radially Gaussian kernel that adapts over time, overcomes these limitations. The method employs a short-time ambiguity function both for kernel optimization and as an intermediate step in computing constant-time slices of the representation. Careful algorithm design provides reasonably efficient computation and allows on-line implementation. Certain enhancements, such as cone-kernel constraints and approximate retention of marginals, are easily incorporated with little additional computation. While somewhat more expensive than fixed-kernel representations, this new technique often provides much better performance. Several examples illustrate its behavior on synthetic and real-world signals.
dc.language.iso eng
dc.subject.otherTime Frequency and Spectral Analysis
dc.title An Adaptive Optimal-Kernel Time-Frequency Representation
dc.type Journal article
dc.citation.bibtexName article
dc.citation.journalTitle IEEE Transactions on Signal Processing 2006-07-24
dc.contributor.orgDigital Signal Processing (
dc.citation.volumeNumber 43
dc.citation.issueNumber 10
dc.type.dcmi Text
dc.type.dcmi Text
dc.citation.firstpage 2361
dc.citation.lastpage 2371

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    Publications by Rice Faculty and graduate students in digital signal processing.
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    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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