Polynomial eigenvalue decomposition for multichannel broadband signal processing

Vincent W. Neo, Soydan Redif, John G. McWhirter, Jennifer Pestana, Ian K. Proudler, Stephan Weiss, Patrick A. Naylor

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This article is devoted to the polynomial eigenvalue decomposition (PEVD) and its applications in broadband multichannel signal processing, motivated by the optimum solutions provided by the eigenvalue decomposition (EVD) for the narrow-band case [1], [2]. In general, the successful techniques from narrowband problems can also be applied to broadband ones, leading to improved solutions. Multichannel broadband signals arise at the core of many essential commercial applications such as telecommunications, speech processing, healthcare monitoring, astronomy and seismic surveillance, and military technologies like radar, sonar and communications [3]. The success of these applications often depends on the performance of signal processing tasks, including data compression [4], source localization [5], channel coding [6], signal enhancement [7], beamforming [8], and source separation [9]. In most cases and for narrowband signals, performing an EVD is the key to the signal processing algorithm. Therefore, this paper aims to introduce PEVD as a novel mathematical technique suitable for many broadband signal processing applications.
Original languageEnglish
Pages (from-to)2-21
Number of pages20
JournalIEEE Signal Processing Magazine
Publication statusAccepted/In press - 4 Apr 2023


  • polynomial matrix
  • polynomial matrix eigen value decomposition
  • multichannel broadband processing
  • space-time covariance matrix
  • lossless filter banks
  • broadband beamforming
  • subband coding
  • speech enchancement


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