Polynomial Matrix Eigenvalue Decomposition of Spherical Harmonics for Speech Enhancement - researchr publication related

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Vincent W. Neo, Christine Evers, Patrick A. Naylor. Polynomial Matrix Eigenvalue Decomposition of Spherical Harmonics for Speech Enhancement. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021, Toronto, ON, Canada, June 6-11, 2021. pages 786-790, IEEE, 2021. [doi]

The following publications are possibly variants of this publication:

Enhancement of Noisy Reverberant Speech Using Polynomial Matrix Eigenvalue DecompositionVincent W. Neo, Christine Evers, Patrick A. Naylor. taslp, 29:3255-3266, 2021. [doi]

Speech Enhancement Using Polynomial Eigenvalue DecompositionVincent W. Neo, Christine Evers, Patrick A. Naylor. waspaa 2019: 125-129 [doi]

Frame-Based Space-Time Covariance Matrix Estimation for Polynomial Eigenvalue Decomposition-Based Speech EnhancementEmilie D'Olne, Vincent W. Neo, Patrick A. Naylor. iwaenc 2022: 1-5 [doi]

Speech Enhancement in Distributed Microphone Arrays Using Polynomial Eigenvalue DecompositionEmilie D'Olne, Vincent W. Neo, Patrick A. Naylor. eusipco 2022: 55-59 [doi]

Polynomial Matrix Eigenvalue Decomposition-Based Source Separation Using Informed Spherical Microphone ArraysVincent W. Neo, Christine Evers, Patrick A. Naylor. waspaa 2021: 201-205 [doi]

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