Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra

Heli Väätäjä, Risto Suoranta, Seppo Rantala

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    4 Citations (Scopus)

    Abstract

    Coherence analysis enables the studying of linear dependencies between multichannel time series. In the case of a multivariate autoregressive (MAR) spectrum the conventional coherence analysis can be applied. However, since we are able to decompose the MAR spectrum, there is a possibility to gain more information through coherence analysis based on conditioned spectra than with conventional methods. The authors formulate the coherence analysis based on the conditioned MAR spectra (reduced and noise conditioned spectra) by giving related definitions for partial and multiple coherences.
    Original languageEnglish
    Title of host publicationProceedings of ICASSP '94
    Subtitle of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages381-384
    ISBN (Print)978-0-7803-1775-8
    DOIs
    Publication statusPublished - 1994
    MoE publication typeA4 Article in a conference publication
    Event1994 IEEE International Conference on Acoustics, Speech and Signal Processing - Adelaide, Australia
    Duration: 19 Apr 199422 Apr 1994

    Conference

    Conference1994 IEEE International Conference on Acoustics, Speech and Signal Processing
    Country/TerritoryAustralia
    CityAdelaide
    Period19/04/9422/04/94

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