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dc.contributor.authorRoy, Sujan Kumar
dc.contributor.authorPaliwal, Kuldip K
dc.date.accessioned2021-11-18T01:43:45Z
dc.date.available2021-11-18T01:43:45Z
dc.date.issued2021
dc.identifier.issn2624-6120en_US
dc.identifier.doi10.3390/signals2030027en_US
dc.identifier.urihttp://hdl.handle.net/10072/410210
dc.description.abstractInaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.en_US
dc.languageenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofpagefrom434en_US
dc.relation.ispartofpageto455en_US
dc.relation.ispartofissue3en_US
dc.relation.ispartofjournalSignalsen_US
dc.relation.ispartofvolume2en_US
dc.relation.urihttp://purl.org/au-research/grants/ARC/DP170102907
dc.relation.grantIDDP170102907en_US
dc.relation.fundersARCen_US
dc.titleRobustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancementen_US
dc.typeJournal articleen_US
dcterms.bibliographicCitationRoy, SK; Paliwal, KK, Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement, Signals, 2 (3), pp. 434-455en_US
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/en_US
dc.date.updated2021-11-16T23:46:38Z
dc.description.versionVersion of Record (VoR)en_US
gro.rights.copyright© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
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gro.griffith.authorPaliwal, Kuldip K.


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