A Smoother State Space Multitaper Spectrogram

A Smoother State Space Multitaper Spectrogram

A recent work (Kim et al. 2018) has reported a novel statistical modeling framework, the State-Space Multitaper (SSMT) method, to estimate time-varying spectral representation of non-stationary time series data. It combines the strengths of the multitaper spectral (MT) analysis paradigm with that of...

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Bibliographic Details
Main Authors: Song, Andrew H. (Author), Chakravarty, Sourish (Author), Brown, Emery Neal (Author)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Picower Institute for Learning and Memory (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor), Massachusetts Institute of Technology. Institute for Medical Engineering & Science (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2019-12-17T21:54:36Z.
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