Imperial College London
Alternate

Mr Mike Brookes

Faculty of Engineering, Department of Electrical and Electronic Engineering

Emeritus Reader
 
 
 
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Contact

 

+44 (0)20 7594 6165mike.brookes Website

 
 
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Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
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Location

 

807aElectrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Dionelis:2018,
author = {Dionelis, N and Brookes, M},
pages = {1642--1646},
publisher = {IEEE},
title = {Speech enhancement using kalman filtering in the logarithmic bark power spectral domain},
url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000455614900330&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We present a phase-sensitive speech enhancement algorithm based on a Kalman filter estimator that tracks speech and noise in the logarithmic Bark power spectral domain. With modulation-domain Kalman filtering, the algorithm tracks the speech spectral log-power using perceptually-motivated Bark bands. By combining STFT bins into Bark bands, the number of frequency components is reduced. The Kalman filter prediction step separately models the inter-frame relations of the speech and noise spectral log-powers and the Kalman filter update step models the nonlinear relations between the speech and noise spectral log-powers using the phase factor in Bark bands, which follows a sub-Gaussian distribution. The posterior mean of the speech spectral log-power is used to create an enhanced speech spectrum for signal reconstruction. The algorithm is evaluated in terms of speech quality and computational complexity with different algorithm configurations compared on various noise types. The algorithm implemented in Bark bands is compared to algorithms implemented in STFT bins and experimental results show that tracking speech in the log Bark power spectral domain, taking into account the temporal dynamics of each subband envelope, is beneficial. Regarding the computational complexity, the percentage decrease in the real-time factor is 44% when using Bark bands compared to when using STFT bins.
AU  - Dionelis,N
AU  - Brookes,M
EP  - 1646
PB  - IEEE
PY  - 2018///
SN  - 2076-1465
SP  - 1642
TI  - Speech enhancement using kalman filtering in the logarithmic bark power spectral domain
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000455614900330&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/68996
ER  -
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