[HTML][HTML] Relevance of polynomial matrix decompositions to broadband blind signal separation
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 …
decomposition (EVD) to polynomial matrices. The purpose of this article is to provide a …
Eigenvalue decomposition of a parahermitian matrix: Extraction of analytic eigenvectors
An analytic parahermitian matrix admits in almost all cases an eigenvalue decomposition
(EVD) with analytic eigenvalues and eigenvectors. We have previously defined a discrete …
(EVD) with analytic eigenvalues and eigenvectors. We have previously defined a discrete …
Signal compaction using polynomial EVD for spherical array processing with applications
Multi-channel signals captured by spatially separated sensors often contain a high level of
data redundancy. A compact signal representation enables more efficient storage and …
data redundancy. A compact signal representation enables more efficient storage and …
Eigenvalue decomposition of a parahermitian matrix: Extraction of analytic eigenvalues
An analytic parahermitian matrix admits an eigenvalue decomposition (EVD) with analytic
eigenvalues and eigenvectors except in the case of multiplexed data. In this paper, we …
eigenvalues and eigenvectors except in the case of multiplexed data. In this paper, we …
Polynomial eigenvalue decomposition for multichannel broadband signal processing: a mathematical technique offering new insights and solutions
This article is devoted to the polynomial eigenvalue decomposition (PEVD) and its
applications in broadband multichannel signal processing, motivated by the optimum …
applications in broadband multichannel signal processing, motivated by the optimum …
On the existence and uniqueness of the eigenvalue decomposition of a parahermitian matrix
This paper addresses the extension of the factorization of a Hermitian matrix by an
eigenvalue decomposition (EVD) to the case of a parahermitian matrix that is analytic at …
eigenvalue decomposition (EVD) to the case of a parahermitian matrix that is analytic at …
Polynomial eigenvalue decomposition-based target speaker voice activity detection in the presence of competing talkers
Voice activity detection (VAD) algorithms are essential for many speech processing
applications, such as speaker diarization, automatic speech recognition, speech …
applications, such as speaker diarization, automatic speech recognition, speech …
Scalable analytic eigenvalue extraction algorithm
FA Khattak, IK Proudler, S Weiss - IEEE Access, 2024 - ieeexplore.ieee.org
Broadband sensor array problems can be formulated using parahermitian polynomial
matrices, and the optimal solution to these problems can be based on the eigenvalue …
matrices, and the optimal solution to these problems can be based on the eigenvalue …
Space-time covariance matrix estimation: Loss of algebraic multiplicities of eigenvalues
FA Khattak, S Weiss, IK Proudler… - 2022 56th Asilomar …, 2022 - ieeexplore.ieee.org
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 …
analytic eigenvalues. This decomposition is key in order to extend the utility of the EVD from …
Enhancement of noisy reverberant speech using polynomial matrix eigenvalue decomposition
Speech enhancement is important for applications such as telecommunications, hearing
aids, automatic speech recognition and voice-controlled systems. Enhancement algorithms …
aids, automatic speech recognition and voice-controlled systems. Enhancement algorithms …