A Radially-Gaussian, Signal-Dependent Time-Frequency Representation

dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)en_US
dc.citation.firstpage3181en_US
dc.citation.lastpage3184en_US
dc.citation.locationToronto, Canadaen_US
dc.citation.volumeNumber5en_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.authorJones, Douglas L.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:34:35Zen_US
dc.date.available2007-10-31T00:34:35Zen_US
dc.date.issued1991-04-01en_US
dc.date.modified2006-06-12en_US
dc.date.note2006-06-12en_US
dc.date.submitted1991-04-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractAn optimization formulation for designing signal-dependent kernels that are based on radially Gaussian functions is presented. The method is based on optimality criteria and is not ad hoc. The procedure is automatic. The optimization criteria are formulated so that the resulting time-frequency distribution (TFD) is insensitive to the time scale and orientation of the signal in time-frequency. Examples demonstrate that the optimal-kernel TFD offers excellent performance for a larger class of signals than any current fixed-kernel representation. The technique performs well in the presence of substantial additive noise, which suggests that it may prove useful for automatic detection of unknown signals in noise. The cost of this technique is only a few times greater than that of the fixed-kernel methods and the 1/0 optimal kernel method.en_US
dc.identifier.citationR. G. Baraniuk and D. L. Jones, "A Radially-Gaussian, Signal-Dependent Time-Frequency Representation," vol. 5, 1991.en_US
dc.identifier.govdoc10.1109/ICASSP.1991.150131en_US
dc.identifier.urihttps://hdl.handle.net/1911/19680en_US
dc.language.isoengen_US
dc.subject.otherDSP for Communicationsen_US
dc.titleA Radially-Gaussian, Signal-Dependent Time-Frequency Representationen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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