Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra

Heli Väätäjä; Risto Suoranta; Seppo Rantala

doi:10.1109/ICASSP.1994.389801

Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra

Heli Väätäjä, Risto Suoranta, Seppo Rantala

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-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 language	English
Title of host publication	Proceedings of ICASSP '94
Subtitle of host publication	IEEE International Conference on Acoustics, Speech and Signal Processing
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	381-384
ISBN (Print)	978-0-7803-1775-8
DOIs	https://doi.org/10.1109/ICASSP.1994.389801
Publication status	Published - 1994
MoE publication type	A4 Article in a conference publication
Event	1994 IEEE International Conference on Acoustics, Speech and Signal Processing - Adelaide, Australia Duration: 19 Apr 1994 → 22 Apr 1994

Conference

Conference	1994 IEEE International Conference on Acoustics, Speech and Signal Processing
Country/Territory	Australia
City	Adelaide
Period	19/04/94 → 22/04/94

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10.1109/ICASSP.1994.389801

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Väätäjä, H., Suoranta, R., & Rantala, S. (1994). Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra. In Proceedings of ICASSP '94: IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 381-384). IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/ICASSP.1994.389801

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title = "Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra",

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.",

author = "Heli V{\"a}{\"a}t{\"a}j{\"a} and Risto Suoranta and Seppo Rantala",

year = "1994",

doi = "10.1109/ICASSP.1994.389801",

language = "English",

isbn = "978-0-7803-1775-8",

pages = "381--384",

booktitle = "Proceedings of ICASSP '94",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "1994 IEEE International Conference on Acoustics, Speech and Signal Processing ; Conference date: 19-04-1994 Through 22-04-1994",

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Väätäjä, H, Suoranta, R & Rantala, S 1994, Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra. in Proceedings of ICASSP '94: IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE Institute of Electrical and Electronic Engineers, pp. 381-384, 1994 IEEE International Conference on Acoustics, Speech and Signal Processing, Adelaide, Australia, 19/04/94. https://doi.org/10.1109/ICASSP.1994.389801

Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra. / Väätäjä, Heli; Suoranta, Risto; Rantala, Seppo.
Proceedings of ICASSP '94: IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE Institute of Electrical and Electronic Engineers, 1994. p. 381-384.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra

AU - Väätäjä, Heli

AU - Suoranta, Risto

AU - Rantala, Seppo

PY - 1994

Y1 - 1994

N2 - 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.

AB - 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.

U2 - 10.1109/ICASSP.1994.389801

DO - 10.1109/ICASSP.1994.389801

M3 - Conference article in proceedings

SN - 978-0-7803-1775-8

SP - 381

EP - 384

BT - Proceedings of ICASSP '94

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 1994 IEEE International Conference on Acoustics, Speech and Signal Processing

Y2 - 19 April 1994 through 22 April 1994

ER -

Coherence analysis of multichannel time series applying conditioned multivariate autoregressive spectra

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