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We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix. Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion. An outer iteration continues until a desired accuracy for the approximation of the extracted eigenvalues has been achieved. The approach is compared to existing algorithms.
|Number of pages||5|
|Publication status||Published - 16 May 2019|
|Event||2019 International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom|
Duration: 12 May 2019 → 17 May 2019
|Conference||2019 International Conference on Acoustics, Speech, and Signal Processing|
|Abbreviated title||ICASSP 2019|
|Period||12/05/19 → 17/05/19|
- parahermitian matrix
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