Modulation-domain Kalman filtering for single-channel speech enhancement

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Title Modulation-domain Kalman filtering for single-channel speech enhancement
Author So, Stephen; Paliwal, Kuldip Kumar
Journal Name Speech Communication
Year Published 2011
Place of publication Netherlands
Publisher Elsevier BV
Abstract In this paper, we investigate the modulation-domain Kalman filter (MDKF) and compare its performance with other time-domain and acoustic-domain speech enhancement methods. In contrast to previously reported modulation domain-enhancement methods based on fixed bandpass filtering, the MDKF is an adaptive and linear MMSE estimator that uses models of the temporal changes of the magnitude spectrum for both speech and noise. Also, because the Kalman filter is a joint magnitude and phase spectrum estimator, under non-stationarity assumptions, it is highly suited for modulation-domain processing, as phase information has been shown to play an important role in the modulation domain. We have found that the Kalman filter is better suited for processing in the modulation-domain, rather than in the time-domain, since the low order linear predictor is sufficient at modelling the dynamics of slow changes in the modulation domain, while being insufficient at modelling the long-term correlation speech information in the time domain. As a result, the MDKF method produces enhanced speech that has very minimal distortion and residual noise, in the ideal case. The results from objective experiments and blind subjective listening tests using the NOIZEUS corpus show that the MDKF (with clean speech parameters) outperforms all the acoustic and time-domain enhancement methods that were evaluated, including the time-domain Kalman filter with clean speech parameters. A practical MDKF that uses the MMSE-STSA method to enhance noisy speech in the acoustic domain prior to LPC analysis was also evaluated and showed promising results.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1016/j.specom.2011.02.001
Volume 53
Issue Number 6
Page from 818
Page to 829
ISSN 0167-6393
Date Accessioned 2011-08-10; 2012-02-17T04:59:52Z
Date Available 2012-02-17T04:59:52Z
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Signal Processing
URI http://hdl.handle.net/10072/42848
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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