In this section
@inproceedings{Neo:2021:10.1109/WASPAA52581.2021.9632722,
author = {Neo, V and Evers, C and Naylor, P},
doi = {10.1109/WASPAA52581.2021.9632722},
pages = {201--205},
publisher = {IEEE},
title = {Polynomial matrix eigenvalue decomposition-based source separation using informed spherical microphone arrays},
url = {http://dx.doi.org/10.1109/WASPAA52581.2021.9632722},
year = {2021}
}
TY - CPAPER
AB - Audio source separation is essential for many applications such as hearing aids, telecommunications, and robot audition. Subspace decomposition approaches using polynomial matrix eigenvalue decomposition (PEVD) algorithms applied to the microphone signals, or lower-dimension eigenbeams for spherical microphone arrays, are effective for speech enhancement. In this work, we extend the work from speech enhancement and propose a PEVD subspace algorithm that uses eigenbeams for source separation. The proposed PEVD-based source separation approach performs comparably with state-of-the-art algorithms, such as those based on independent component analysis (ICA) and multi-channel non-negative matrix factorization (MNMF). Informal listening examples also indicate that our method does not introduce any audible artifacts.
AU - Neo,V
AU - Evers,C
AU - Naylor,P
DO - 10.1109/WASPAA52581.2021.9632722
EP - 205
PB - IEEE
PY - 2021///
SP - 201
TI - Polynomial matrix eigenvalue decomposition-based source separation using informed spherical microphone arrays
UR - http://dx.doi.org/10.1109/WASPAA52581.2021.9632722
UR - https://ieeexplore.ieee.org/document/9632722
UR - http://hdl.handle.net/10044/1/90863
ER -
For more information about the group, please contact:
Dr Dan Goodman
+44 (0)20 7594 6264
d.goodman@imperial.ac.uk