In this section
@inproceedings{Neo:2021:10.1109/ICASSP39728.2021.9414011,
author = {Neo, VW and Evers, C and Naylor, PA},
doi = {10.1109/ICASSP39728.2021.9414011},
pages = {786--790},
publisher = {IEEE},
title = {Polynomial matrix eigenvalue decomposition of spherical harmonics for speech enhancement},
url = {http://dx.doi.org/10.1109/ICASSP39728.2021.9414011},
year = {2021}
}
TY - CPAPER
AB - Speech enhancement algorithms using polynomial matrix eigen value decomposition (PEVD) have been shown to be effective for noisy and reverberant speech. However, these algorithms do not scale well in complexity with the number of channels used in the processing. For a spherical microphone array sampling an order-limited sound field, the spherical harmonics provide a compact representation of the microphone signals in the form of eigen beams. We propose a PEVD algorithm that uses only the lower dimension eigen beams for speech enhancement at a significantly lower computation cost. The proposed algorithm is shown to significantly reduce complexity while maintaining full performance. Informal listening examples have also indicated that the processing does not introduce any noticeable artefacts.
AU - Neo,VW
AU - Evers,C
AU - Naylor,PA
DO - 10.1109/ICASSP39728.2021.9414011
EP - 790
PB - IEEE
PY - 2021///
SP - 786
TI - Polynomial matrix eigenvalue decomposition of spherical harmonics for speech enhancement
UR - http://dx.doi.org/10.1109/ICASSP39728.2021.9414011
UR - http://hdl.handle.net/10044/1/87563
ER -
Speech and Audio Processing Lab
CSP Group, EEE Department
Imperial College London
Exhibition Road, London, SW7 2AZ, United Kingdom