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Graph linear prediction results in smaller error than standard linear prediction
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-1285-8947
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2718-0262
2015 (English)In: 2015 23rd European Signal Processing Conference, EUSIPCO 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015, 220-224 p.Conference paper, Published paper (Refereed)
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Text
Abstract [en]

Linear prediction is a popular strategy employed in the analysis and representation of signals. In this paper, we propose a new linear prediction approach by considering the standard linear prediction in the context of graph signal processing, which has gained significant attention recently. We view the signal to be defined on the nodes of a graph with an adjacency matrix constructed using the coefficients of the standard linear predictor (SLP). We prove theoretically that the graph based linear prediction approach results in an equal or better performance compared with the SLP in terms of the prediction gain. We illustrate the proposed concepts by application to real speech signals.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. 220-224 p.
Keyword [en]
autoregressive model, Graph signal processing, Linear prediction, Forecasting, Graphic methods, Adjacency matrices, Auto regressive models, Graph-based, Linear prediction approaches, Linear predictors, Real-speech signals, Signal processing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-186792DOI: 10.1109/EUSIPCO.2015.7362377ISI: 000377943800045Scopus ID: 2-s2.0-84963976259ISBN: 9780992862633 (print)OAI: oai:DiVA.org:kth-186792DiVA: diva2:938619
Conference
23rd European Signal Processing Conference, EUSIPCO 2015, 31 August 2015 through 4 September 2015
Note

QC 20160617

Available from: 2016-06-17 Created: 2016-05-13 Last updated: 2016-07-18Bibliographically approved

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Venkitaraman, ArunChatterjee, SaikatHändel, Peter
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Citation style
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