Block-Recursive IAA-based Spectral Estimates with Missing Samples using data interpolation

George Glentis, Andreas Jakobsson, Kostas Angelopoulos

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

4 Citations (SciVal)

Abstract

In this work, we examine a computationally efficient block-updating scheme for estimating the spectral content of signals with missing samples. The work is an extension of our recent single-sample data interpolation updating of the Iterative Adaptive Approach (IAA), being reformulated to incorporate blocks of samples. The proposed implementation offers a substantial complexity reduction as compared to earlier presented updating schemes, without sacrificing the quality of the resulting spectral estimates more than marginally (if at all).
Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages350-354
Number of pages5
ISBN (Print)978-1-4799-2892-7
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) - Florence, Italy
Duration: 2014 May 42014 May 9

Conference

Conference2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)
Country/TerritoryItaly
CityFlorence
Period2014/05/042014/05/09

Subject classification (UKÄ)

  • Probability Theory and Statistics

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