Time-Frequency Complexity and Information

dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)en_US
dc.citation.firstpageIII-329en_US
dc.citation.lastpageIII-332en_US
dc.citation.locationAdelaide, Australiaen_US
dc.citation.volumeNumber3en_US
dc.contributor.authorFlandrin, Patricken_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.authorMichel, Olivieren_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:44:01Zen_US
dc.date.available2007-10-31T00:44:01Zen_US
dc.date.issued1994-04-01en_US
dc.date.modified2006-06-12en_US
dc.date.note2004-11-08en_US
dc.date.submitted1994-04-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractMany functions have been proposed for estimating signal information content and complexity on the time-frequency plane, including moment-based measures such as the time-bandwidth product and the Shannon and Renyi entropies. When applied to a time-frequency representation from Cohen's class, the Renyi entropy conforms closely to the visually based notion of complexity that we use when inspecting time-frequency images. A detailed discussion reveals many of the desirable properties of the Renyi information measure for both deterministic and random signals.en_US
dc.identifier.citationP. Flandrin, R. G. Baraniuk and O. Michel, "Time-Frequency Complexity and Information," vol. 3, 1994.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICASSP.1994.390031en_US
dc.identifier.urihttps://hdl.handle.net/1911/19885en_US
dc.language.isoengen_US
dc.subjecttime frequencyen_US
dc.subjectcomplexityen_US
dc.subject.keywordtime frequencyen_US
dc.subject.keywordcomplexityen_US
dc.subject.otherTime Frequency and Spectral Analysisen_US
dc.titleTime-Frequency Complexity and Informationen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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