The polynomial matrix EVD (PEVD) is an extension of the conventional eigenvalue
decomposition (EVD) to polynomial matrices. The purpose of this article is to provide a …

For parahermitian polynomial matrices, which can be used, for example, to characterize
space-time covariance in broadband array processing, the conventional eigenvalue …

Direction of arrival (DoA) estimation for sound source localization is increasingly prevalent in
modern devices. In this paper, we explore a polynomial extension to the multiple signal …

Parahermitian matrices in almost all cases admit an eigenvalue decomposition (EVD) with
analytic eigenvalues. This decomposition is key in order to extend the utility of the EVD from …

A polynomial eigenvalue decomposition of paraher-mitian matrices can be calculated
approximately using iterative approaches such as the sequential matrix diagonalisation …

Speech enhancement is important for applications such as telecommunications, hearing
aids, automatic speech recognition and voice-controlled systems. Enhancement algorithms …

This paper presents a novel method for calculating a compact order singular value
decomposition (SVD) of polynomial matrices, building upon the recently proven existence of …

The Second-order Sequential Best Rotation (SBR2) algorithm, used for Eigenvalue
Decomposition (EVD) on para-Hermitian polynomial matrices typically encountered in …

Extracting analytic eigenvectors from parahermitian matrices relies on phase smoothing in
the discrete Fourier transform (DFT) domain as its most expensive algorithmic component …

In order to determine the analytic eigenvalues of a parahermitian matrix, the state-of-the-art
algorithm offers proven convergence but its complexity grows factorially with the matrix …