Imperial College London
Alternate

Patrick A. Naylor

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Speech & Acoustic Signal Processing
 
 
 
//

Contact

 

+44 (0)20 7594 6235p.naylor Website

 
 
//

Location

 

803Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Neo:2021:10.1109/TASLP.2021.3120630,
author = {Neo, V and Evers, C and Naylor, P},
doi = {10.1109/TASLP.2021.3120630},
journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing},
pages = {3255--3266},
title = {Enhancement of noisy reverberant speech using polynomial matrix eigenvalue decomposition},
url = {http://dx.doi.org/10.1109/TASLP.2021.3120630},
volume = {29},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Speech enhancement is important for applications such as telecommunications, hearing aids, automatic speech recognition and voice-controlled systems. Enhancement algorithms aim to reduce interfering noise and reverberation while minimizing any speech distortion. In this work for speech enhancement, we propose to use polynomial matrices to model the spatial, spectral and temporal correlations between the speech signals received by a microphone array and polynomial matrix eigenvalue decomposition (PEVD) to decorrelate in space, time and frequency simultaneously. We then propose a blind and unsupervised PEVD-based speech enhancement algorithm. Simulations and informal listening examples involving diverse reverberant and noisy environments have shown that our method can jointly suppress noise and reverberation, thereby achieving speech enhancement without introducing processing artefacts into the enhanced signal.
AU  - Neo,V
AU  - Evers,C
AU  - Naylor,P
DO  - 10.1109/TASLP.2021.3120630
EP  - 3266
PY  - 2021///
SN  - 2329-9290
SP  - 3255
TI  - Enhancement of noisy reverberant speech using polynomial matrix eigenvalue decomposition
T2  - IEEE/ACM Transactions on Audio, Speech and Language Processing
UR  - http://dx.doi.org/10.1109/TASLP.2021.3120630
UR  - https://ieeexplore.ieee.org/document/9576653
UR  - http://hdl.handle.net/10044/1/92621
VL  - 29
ER  -
Download